Technical
Technical Service Documentation
Video Intelligence Service - Technical Implementation

Video Intelligence Service - Technical Implementation

The Video Intelligence Service provides advanced computer vision and video analytics capabilities for real-time video processing, content analysis, and intelligent video workflows.

Service Overview

The Video Intelligence Service combines computer vision, machine learning, and streaming technologies to provide comprehensive video analysis capabilities. It supports real-time processing, batch analysis, content understanding, and intelligent video workflows for various enterprise use cases.

Key Capabilities

  • Real-time Video Processing: Live stream analysis with low-latency processing
  • Computer Vision Pipeline: Object detection, face recognition, scene analysis, and OCR
  • Content Intelligence: Video summarization, highlight detection, and content classification
  • Stream Analytics: Performance monitoring, quality assessment, and engagement analytics
  • Multi-format Support: Support for various video formats, codecs, and streaming protocols
  • Edge Processing: Distributed processing for bandwidth optimization and privacy

Architecture Design

Core Components 

  graph TB
    A[Video Ingestion] --> B[Stream Processor]
    B --> C[Computer Vision Engine]
    C --> D[AI Analysis Pipeline]
    D --> E[Results Aggregator]
    E --> F[Output Adapters]
    
    G[Model Registry] --> C
    H[Frame Buffer] --> B
    I[Metadata Store] --> E
    J[Stream Manager] --> B
    K[Performance Monitor] --> F

System Architecture 

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// Core service architecture
@Service
@Component("videoIntelligenceService")
class VideoIntelligenceService(
    private val videoIngestionService: VideoIngestionService,
    private val streamProcessor: StreamProcessor,
    private val visionEngine: ComputerVisionEngine,
    private val analysisOrchestrator: AnalysisOrchestrator,
    private val resultsAggregator: ResultsAggregator,
    private val streamManager: StreamManager,
    private val modelRegistry: ModelRegistry
) {
    
    suspend fun createAnalysisJob(request: VideoAnalysisRequest): VideoAnalysisResponse {
        // Validate analysis request
        validateAnalysisRequest(request)
        
        // Create analysis job
        val job = VideoAnalysisJob(
            id = UUID.randomUUID().toString(),
            name = request.name,
            config = request.config,
            status = AnalysisJobStatus.CREATING
        )
        
        // Start video ingestion
        val stream = videoIngestionService.ingestVideo(request.videoSource)
        
        // Create processing pipeline
        val pipeline = analysisOrchestrator.createPipeline(job, stream)
        
        // Start analysis
        analysisOrchestrator.startAnalysis(pipeline)
        
        return VideoAnalysisResponse(
            jobId = job.id,
            status = job.status,
            streamUrl = stream.url,
            createdAt = job.createdAt
        )
    }
}

API Specifications

REST API Endpoints

Video Analysis Management 

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# OpenAPI Specification
openapi: 3.0.3
info:
  title: Video Intelligence Service API
  version: 1.0.0
  description: Advanced video processing and computer vision analytics service

paths:
  /api/v1/analysis/jobs:
    post:
      summary: Create video analysis job
      operationId: createAnalysisJob
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/VideoAnalysisRequest'
      responses:
        '201':
          description: Analysis job created
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/VideoAnalysisResponse'
        '400':
          description: Invalid analysis request
        '429':
          description: Processing quota exceeded

    get:
      summary: List analysis jobs
      parameters:
        - name: status
          in: query
          schema:
            type: string
            enum: [creating, running, completed, failed, cancelled]
        - name: type
          in: query
          schema:
            type: string
            enum: [real-time, batch, on-demand]
        - name: created_after
          in: query
          schema:
            type: string
            format: date-time
        - name: limit
          in: query
          schema:
            type: integer
            default: 50
        - name: offset
          in: query
          schema:
            type: integer
            default: 0
      responses:
        '200':
          description: List of analysis jobs
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/AnalysisJobList'

  /api/v1/analysis/jobs/{jobId}:
    get:
      summary: Get analysis job details
      parameters:
        - name: jobId
          in: path
          required: true
          schema:
            type: string
      responses:
        '200':
          description: Analysis job details
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/AnalysisJob'
        '404':
          description: Analysis job not found

    patch:
      summary: Update analysis job configuration
      parameters:
        - name: jobId
          in: path
          required: true
          schema:
            type: string
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/AnalysisJobUpdateRequest'
      responses:
        '200':
          description: Job updated
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/AnalysisJob'

    delete:
      summary: Cancel/delete analysis job
      parameters:
        - name: jobId
          in: path
          required: true
          schema:
            type: string
      responses:
        '204':
          description: Job cancelled
        '404':
          description: Job not found
        '409':
          description: Cannot cancel completed job

  /api/v1/analysis/jobs/{jobId}/results:
    get:
      summary: Get analysis results
      parameters:
        - name: jobId
          in: path
          required: true
          schema:
            type: string
        - name: result_type
          in: query
          schema:
            type: string
            enum: [objects, faces, scenes, text, summary]
        - name: time_range
          in: query
          schema:
            type: string
            description: "Format: start_time-end_time in seconds"
        - name: confidence_threshold
          in: query
          schema:
            type: number
            minimum: 0
            maximum: 1
            default: 0.5
        - name: limit
          in: query
          schema:
            type: integer
            default: 100
      responses:
        '200':
          description: Analysis results
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/AnalysisResults'
        '404':
          description: Job not found

  /api/v1/analysis/jobs/{jobId}/timeline:
    get:
      summary: Get analysis timeline
      parameters:
        - name: jobId
          in: path
          required: true
          schema:
            type: string
        - name: granularity
          in: query
          schema:
            type: string
            enum: [second, minute, scene]
            default: second
      responses:
        '200':
          description: Analysis timeline
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/AnalysisTimeline'

  /api/v1/streams:
    post:
      summary: Create video stream
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/StreamCreationRequest'
      responses:
        '201':
          description: Stream created
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/VideoStream'

    get:
      summary: List video streams
      parameters:
        - name: status
          in: query
          schema:
            type: string
            enum: [active, inactive, error]
        - name: type
          in: query
          schema:
            type: string
            enum: [rtmp, webrtc, hls, dash]
      responses:
        '200':
          description: Video streams
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/VideoStreamList'

  /api/v1/streams/{streamId}:
    get:
      summary: Get stream details
      parameters:
        - name: streamId
          in: path
          required: true
          schema:
            type: string
      responses:
        '200':
          description: Stream details
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/VideoStream'

    delete:
      summary: Stop video stream
      parameters:
        - name: streamId
          in: path
          required: true
          schema:
            type: string
      responses:
        '204':
          description: Stream stopped

  /api/v1/streams/{streamId}/analytics:
    get:
      summary: Get stream analytics
      parameters:
        - name: streamId
          in: path
          required: true
          schema:
            type: string
        - name: time_range
          in: query
          schema:
            type: string
            enum: [1h, 6h, 24h, 7d, 30d]
            default: 1h
        - name: metrics
          in: query
          schema:
            type: array
            items:
              type: string
              enum: [quality, latency, viewers, engagement, detections]
      responses:
        '200':
          description: Stream analytics
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/StreamAnalytics'

  /api/v1/models:
    get:
      summary: List available models
      parameters:
        - name: category
          in: query
          schema:
            type: string
            enum: [object_detection, face_recognition, scene_classification, ocr, action_recognition]
        - name: framework
          in: query
          schema:
            type: string
            enum: [tensorflow, pytorch, onnx, openvino]
      responses:
        '200':
          description: Available models
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ModelList'

    post:
      summary: Deploy custom model
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/ModelDeploymentRequest'
      responses:
        '201':
          description: Model deployed
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/Model'

  /api/v1/models/{modelId}:
    get:
      summary: Get model details
      parameters:
        - name: modelId
          in: path
          required: true
          schema:
            type: string
      responses:
        '200':
          description: Model details
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/Model'

  /api/v1/models/{modelId}/benchmark:
    post:
      summary: Benchmark model performance
      parameters:
        - name: modelId
          in: path
          required: true
          schema:
            type: string
      requestBody:
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/BenchmarkRequest'
      responses:
        '200':
          description: Benchmark results
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/BenchmarkResults'

  /api/v1/processing/batch:
    post:
      summary: Submit batch processing job
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/BatchProcessingRequest'
      responses:
        '202':
          description: Batch job submitted
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/BatchJobResponse'

  /api/v1/processing/real-time:
    post:
      summary: Start real-time processing
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/RealTimeProcessingRequest'
      responses:
        '200':
          description: Real-time processing started
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/RealTimeProcessingResponse'

components:
  schemas:
    VideoAnalysisRequest:
      type: object
      required:
        - name
        - video_source
        - analysis_config
      properties:
        name:
          type: string
          example: "security-camera-analysis"
        description:
          type: string
        video_source:
          $ref: '#/components/schemas/VideoSource'
        analysis_config:
          $ref: '#/components/schemas/AnalysisConfig'
        processing_mode:
          type: string
          enum: [real-time, batch, on-demand]
          default: real-time
        priority:
          type: string
          enum: [low, medium, high, critical]
          default: medium
        tags:
          type: array
          items:
            type: string

    VideoSource:
      type: object
      required:
        - type
        - url
      properties:
        type:
          type: string
          enum: [rtmp, webrtc, hls, dash, file, camera]
        url:
          type: string
          format: uri
          example: "rtmp://stream.example.com/live/stream123"
        credentials:
          type: object
          properties:
            username:
              type: string
            password:
              type: string
              format: password
            token:
              type: string
        quality_settings:
          $ref: '#/components/schemas/QualitySettings'

    AnalysisConfig:
      type: object
      properties:
        models:
          type: array
          items:
            $ref: '#/components/schemas/ModelConfig'
          description: "Models to apply during analysis"
        frame_sampling:
          $ref: '#/components/schemas/FrameSamplingConfig'
        output_config:
          $ref: '#/components/schemas/OutputConfig'
        performance_config:
          $ref: '#/components/schemas/PerformanceConfig'

    ModelConfig:
      type: object
      required:
        - model_id
        - enabled
      properties:
        model_id:
          type: string
          example: "yolov8-object-detection"
        enabled:
          type: boolean
        confidence_threshold:
          type: number
          minimum: 0
          maximum: 1
          default: 0.5
        region_of_interest:
          $ref: '#/components/schemas/RegionOfInterest'
        custom_parameters:
          type: object
          additionalProperties: true

    FrameSamplingConfig:
      type: object
      properties:
        sampling_rate:
          type: number
          description: "Frames per second to process"
          default: 1
        key_frame_only:
          type: boolean
          default: false
        adaptive_sampling:
          type: boolean
          description: "Adjust sampling based on content complexity"
          default: false

    AnalysisJob:
      type: object
      properties:
        id:
          type: string
          format: uuid
        name:
          type: string
        description:
          type: string
        status:
          type: string
          enum: [creating, running, completed, failed, cancelled]
        video_source:
          $ref: '#/components/schemas/VideoSource'
        analysis_config:
          $ref: '#/components/schemas/AnalysisConfig'
        progress:
          $ref: '#/components/schemas/JobProgress'
        statistics:
          $ref: '#/components/schemas/JobStatistics'
        created_at:
          type: string
          format: date-time
        started_at:
          type: string
          format: date-time
        completed_at:
          type: string
          format: date-time

    AnalysisResults:
      type: object
      properties:
        job_id:
          type: string
        result_type:
          type: string
        time_range:
          $ref: '#/components/schemas/TimeRange'
        results:
          type: array
          items:
            $ref: '#/components/schemas/DetectionResult'
        summary:
          $ref: '#/components/schemas/ResultSummary'
        metadata:
          type: object
          additionalProperties: true

    DetectionResult:
      type: object
      properties:
        timestamp:
          type: number
          description: "Time in seconds from video start"
        frame_number:
          type: integer
        model_id:
          type: string
        detections:
          type: array
          items:
            $ref: '#/components/schemas/Detection'
        metadata:
          type: object
          additionalProperties: true

    Detection:
      type: object
      properties:
        class_name:
          type: string
          example: "person"
        confidence:
          type: number
          minimum: 0
          maximum: 1
        bounding_box:
          $ref: '#/components/schemas/BoundingBox'
        attributes:
          type: object
          additionalProperties: true
          example:
            age_range: "25-35"
            gender: "female"
            emotion: "happy"

    BoundingBox:
      type: object
      required:
        - x
        - y
        - width
        - height
      properties:
        x:
          type: number
          minimum: 0
          maximum: 1
          description: "X coordinate (normalized 0-1)"
        y:
          type: number
          minimum: 0
          maximum: 1
          description: "Y coordinate (normalized 0-1)"
        width:
          type: number
          minimum: 0
          maximum: 1
          description: "Width (normalized 0-1)"
        height:
          type: number
          minimum: 0
          maximum: 1
          description: "Height (normalized 0-1)"

    VideoStream:
      type: object
      properties:
        id:
          type: string
          format: uuid
        name:
          type: string
        type:
          type: string
          enum: [rtmp, webrtc, hls, dash]
        status:
          type: string
          enum: [active, inactive, error]
        source_url:
          type: string
        output_urls:
          type: object
          additionalProperties:
            type: string
        quality_profiles:
          type: array
          items:
            $ref: '#/components/schemas/QualityProfile'
        analytics:
          $ref: '#/components/schemas/StreamAnalytics'
        created_at:
          type: string
          format: date-time

    StreamAnalytics:
      type: object
      properties:
        stream_id:
          type: string
        time_range:
          $ref: '#/components/schemas/TimeRange'
        metrics:
          type: object
          properties:
            bitrate:
              type: object
              properties:
                current:
                  type: number
                average:
                  type: number
                peak:
                  type: number
            latency:
              type: object
              properties:
                current_ms:
                  type: number
                average_ms:
                  type: number
                p95_ms:
                  type: number
            frame_rate:
              type: object
              properties:
                current_fps:
                  type: number
                target_fps:
                  type: number
                dropped_frames:
                  type: integer
            viewers:
              type: object
              properties:
                current:
                  type: integer
                peak:
                  type: integer
                total:
                  type: integer
        detections:
          type: object
          properties:
            total_detections:
              type: integer
            detections_per_minute:
              type: number
            top_classes:
              type: array
              items:
                type: object
                properties:
                  class_name:
                    type: string
                  count:
                    type: integer

    Model:
      type: object
      properties:
        id:
          type: string
        name:
          type: string
        category:
          type: string
        framework:
          type: string
        version:
          type: string
        description:
          type: string
        input_format:
          $ref: '#/components/schemas/InputFormat'
        output_format:
          $ref: '#/components/schemas/OutputFormat'
        performance_metrics:
          $ref: '#/components/schemas/PerformanceMetrics'
        supported_hardware:
          type: array
          items:
            type: string
            enum: [cpu, gpu, tpu, vpu]
        model_size_mb:
          type: number
        created_at:
          type: string
          format: date-time

  securitySchemes:
    BearerAuth:
      type: http
      scheme: bearer
      bearerFormat: JWT
    ApiKeyAuth:
      type: apiKey
      in: header
      name: X-API-Key

security:
  - BearerAuth: []
  - ApiKeyAuth: []

GraphQL API 

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# GraphQL schema for video intelligence
type Query {
  analysisJobs(
    filter: AnalysisJobFilter
    sort: AnalysisJobSort
    pagination: PaginationInput
  ): AnalysisJobConnection!
  
  analysisJob(id: ID!): AnalysisJob
  
  analysisResults(
    jobId: ID!
    filter: ResultsFilter
    pagination: PaginationInput
  ): AnalysisResultsConnection!
  
  videoStreams(
    filter: VideoStreamFilter
    pagination: PaginationInput
  ): VideoStreamConnection!
  
  videoStream(id: ID!): VideoStream
  
  models(
    filter: ModelFilter
    pagination: PaginationInput
  ): ModelConnection!
  
  model(id: ID!): Model
  
  streamAnalytics(
    streamId: ID!
    timeRange: TimeRange!
    metrics: [AnalyticsMetric!]
  ): StreamAnalytics!
}

type Mutation {
  createAnalysisJob(input: AnalysisJobInput!): AnalysisJobPayload!
  updateAnalysisJob(id: ID!, input: AnalysisJobUpdateInput!): AnalysisJobPayload!
  cancelAnalysisJob(id: ID!): AnalysisJobPayload!
  
  createVideoStream(input: VideoStreamInput!): VideoStreamPayload!
  updateVideoStream(id: ID!, input: VideoStreamUpdateInput!): VideoStreamPayload!
  stopVideoStream(id: ID!): VideoStreamPayload!
  
  deployModel(input: ModelDeploymentInput!): ModelPayload!
  updateModel(id: ID!, input: ModelUpdateInput!): ModelPayload!
  
  benchmarkModel(id: ID!, input: BenchmarkInput!): BenchmarkPayload!
}

type Subscription {
  analysisJobUpdates(jobId: ID!): AnalysisJobUpdate!
  analysisResults(jobId: ID!): AnalysisResultUpdate!
  streamAnalytics(streamId: ID!): StreamAnalyticsUpdate!
  modelPerformance(modelId: ID!): ModelPerformanceUpdate!
}

# Core types
type AnalysisJob {
  id: ID!
  name: String!
  description: String
  status: AnalysisJobStatus!
  videoSource: VideoSource!
  analysisConfig: AnalysisConfig!
  progress: JobProgress
  statistics: JobStatistics
  results: AnalysisResultsConnection!
  createdAt: DateTime!
  startedAt: DateTime
  completedAt: DateTime
  estimatedDuration: Duration
}

enum AnalysisJobStatus {
  CREATING
  RUNNING
  COMPLETED
  FAILED
  CANCELLED
}

type VideoSource {
  type: VideoSourceType!
  url: String!
  credentials: Credentials
  qualitySettings: QualitySettings
}

enum VideoSourceType {
  RTMP
  WEBRTC
  HLS
  DASH
  FILE
  CAMERA
}

type AnalysisConfig {
  models: [ModelConfig!]!
  frameSampling: FrameSamplingConfig!
  outputConfig: OutputConfig!
  performanceConfig: PerformanceConfig!
}

type DetectionResult {
  timestamp: Float!
  frameNumber: Int!
  modelId: String!
  detections: [Detection!]!
  metadata: JSON
}

type Detection {
  className: String!
  confidence: Float!
  boundingBox: BoundingBox!
  attributes: JSON
  trackingId: String
}

type VideoStream {
  id: ID!
  name: String!
  type: VideoStreamType!
  status: StreamStatus!
  sourceUrl: String!
  outputUrls: JSON!
  qualityProfiles: [QualityProfile!]!
  analytics: StreamAnalytics
  analysisJobs: [AnalysisJob!]!
  createdAt: DateTime!
}

# Custom scalars
scalar DateTime
scalar Duration
scalar JSON

gRPC Service Definition 

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// video_intelligence.proto
syntax = "proto3";

package aimatrix.video.v1;

import "google/protobuf/timestamp.proto";
import "google/protobuf/duration.proto";
import "google/protobuf/struct.proto";
import "google/protobuf/empty.proto";

// Video Intelligence Service
service VideoIntelligenceService {
  // Analysis job management
  rpc CreateAnalysisJob(CreateAnalysisJobRequest) returns (CreateAnalysisJobResponse);
  rpc GetAnalysisJob(GetAnalysisJobRequest) returns (AnalysisJob);
  rpc ListAnalysisJobs(ListAnalysisJobsRequest) returns (ListAnalysisJobsResponse);
  rpc CancelAnalysisJob(CancelAnalysisJobRequest) returns (google.protobuf.Empty);
  
  // Results retrieval
  rpc GetAnalysisResults(GetAnalysisResultsRequest) returns (GetAnalysisResultsResponse);
  rpc StreamAnalysisResults(StreamAnalysisResultsRequest) returns (stream AnalysisResultUpdate);
  
  // Stream management
  rpc CreateVideoStream(CreateVideoStreamRequest) returns (VideoStream);
  rpc GetVideoStream(GetVideoStreamRequest) returns (VideoStream);
  rpc StopVideoStream(StopVideoStreamRequest) returns (google.protobuf.Empty);
  
  // Real-time processing
  rpc ProcessVideoFrame(ProcessVideoFrameRequest) returns (ProcessVideoFrameResponse);
  rpc StreamVideoProcessing(stream VideoFrameRequest) returns (stream ProcessingResult);
}

// Model Management Service
service ModelService {
  rpc ListModels(ListModelsRequest) returns (ListModelsResponse);
  rpc GetModel(GetModelRequest) returns (Model);
  rpc DeployModel(DeployModelRequest) returns (Model);
  rpc BenchmarkModel(BenchmarkModelRequest) returns (BenchmarkResults);
}

// Stream Analytics Service
service StreamAnalyticsService {
  rpc GetStreamAnalytics(GetStreamAnalyticsRequest) returns (StreamAnalytics);
  rpc StreamAnalyticsUpdates(StreamAnalyticsUpdatesRequest) returns (stream AnalyticsUpdate);
}

// Request/Response Messages
message CreateAnalysisJobRequest {
  string name = 1;
  string description = 2;
  VideoSource video_source = 3;
  AnalysisConfig analysis_config = 4;
  ProcessingMode processing_mode = 5;
  Priority priority = 6;
  repeated string tags = 7;
}

message CreateAnalysisJobResponse {
  string job_id = 1;
  AnalysisJobStatus status = 2;
  google.protobuf.Timestamp created_at = 3;
  google.protobuf.Duration estimated_duration = 4;
}

message AnalysisJob {
  string id = 1;
  string name = 2;
  string description = 3;
  AnalysisJobStatus status = 4;
  VideoSource video_source = 5;
  AnalysisConfig analysis_config = 6;
  JobProgress progress = 7;
  JobStatistics statistics = 8;
  google.protobuf.Timestamp created_at = 9;
  google.protobuf.Timestamp started_at = 10;
  google.protobuf.Timestamp completed_at = 11;
}

message VideoSource {
  VideoSourceType type = 1;
  string url = 2;
  Credentials credentials = 3;
  QualitySettings quality_settings = 4;
}

message AnalysisConfig {
  repeated ModelConfig models = 1;
  FrameSamplingConfig frame_sampling = 2;
  OutputConfig output_config = 3;
  PerformanceConfig performance_config = 4;
}

message DetectionResult {
  double timestamp = 1;
  int64 frame_number = 2;
  string model_id = 3;
  repeated Detection detections = 4;
  google.protobuf.Struct metadata = 5;
}

message Detection {
  string class_name = 1;
  float confidence = 2;
  BoundingBox bounding_box = 3;
  google.protobuf.Struct attributes = 4;
  string tracking_id = 5;
}

message BoundingBox {
  float x = 1;
  float y = 2;
  float width = 3;
  float height = 4;
}

// Enums
enum AnalysisJobStatus {
  ANALYSIS_JOB_STATUS_UNSPECIFIED = 0;
  ANALYSIS_JOB_STATUS_CREATING = 1;
  ANALYSIS_JOB_STATUS_RUNNING = 2;
  ANALYSIS_JOB_STATUS_COMPLETED = 3;
  ANALYSIS_JOB_STATUS_FAILED = 4;
  ANALYSIS_JOB_STATUS_CANCELLED = 5;
}

enum VideoSourceType {
  VIDEO_SOURCE_TYPE_UNSPECIFIED = 0;
  VIDEO_SOURCE_TYPE_RTMP = 1;
  VIDEO_SOURCE_TYPE_WEBRTC = 2;
  VIDEO_SOURCE_TYPE_HLS = 3;
  VIDEO_SOURCE_TYPE_DASH = 4;
  VIDEO_SOURCE_TYPE_FILE = 5;
  VIDEO_SOURCE_TYPE_CAMERA = 6;
}

enum ProcessingMode {
  PROCESSING_MODE_UNSPECIFIED = 0;
  PROCESSING_MODE_REAL_TIME = 1;
  PROCESSING_MODE_BATCH = 2;
  PROCESSING_MODE_ON_DEMAND = 3;
}

enum Priority {
  PRIORITY_UNSPECIFIED = 0;
  PRIORITY_LOW = 1;
  PRIORITY_MEDIUM = 2;
  PRIORITY_HIGH = 3;
  PRIORITY_CRITICAL = 4;
}

Implementation Examples

Kotlin/Spring Boot Service Implementation 

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// Main service implementation
@Service
@Transactional
@Component("videoIntelligenceService")
class VideoIntelligenceServiceImpl(
    private val analysisJobRepository: AnalysisJobRepository,
    private val videoIngestionService: VideoIngestionService,
    private val streamProcessor: StreamProcessor,
    private val visionEngine: ComputerVisionEngine,
    private val analysisOrchestrator: AnalysisOrchestrator,
    private val resultsAggregator: ResultsAggregator,
    private val streamManager: StreamManager,
    private val modelRegistry: ModelRegistry,
    private val frameBufferService: FrameBufferService,
    private val metricsCollector: MetricsCollector
) : VideoIntelligenceService {

    private val logger = LoggerFactory.getLogger(VideoIntelligenceServiceImpl::class.java)

    @Async
    override suspend fun createAnalysisJob(request: VideoAnalysisRequest): VideoAnalysisResponse {
        logger.info("Creating video analysis job: ${request.name}")
        
        // Validate request
        validateAnalysisRequest(request)
        
        // Create analysis job entity
        val job = VideoAnalysisJobEntity(
            id = UUID.randomUUID().toString(),
            name = request.name,
            description = request.description,
            videoSource = request.videoSource,
            analysisConfig = request.analysisConfig,
            processingMode = request.processingMode,
            priority = request.priority,
            status = AnalysisJobStatus.CREATING,
            tags = request.tags,
            createdAt = Instant.now()
        )
        
        analysisJobRepository.save(job)
        
        try {
            // Initialize video ingestion
            val stream = videoIngestionService.createVideoStream(
                VideoStreamRequest(
                    source = request.videoSource,
                    quality = request.analysisConfig.qualitySettings,
                    buffering = request.analysisConfig.bufferingConfig
                )
            )
            
            // Create processing pipeline
            val pipeline = analysisOrchestrator.createProcessingPipeline(
                job = job,
                stream = stream,
                models = loadModels(request.analysisConfig.models)
            )
            
            // Start analysis based on processing mode
            when (request.processingMode) {
                ProcessingMode.REAL_TIME -> startRealTimeAnalysis(pipeline)
                ProcessingMode.BATCH -> scheduleBatchAnalysis(pipeline)
                ProcessingMode.ON_DEMAND -> prepareOnDemandAnalysis(pipeline)
            }
            
            // Update job status
            job.status = AnalysisJobStatus.RUNNING
            job.startedAt = Instant.now()
            job.streamId = stream.id
            job.pipelineId = pipeline.id
            
            analysisJobRepository.save(job)
            
            // Start metrics collection
            metricsCollector.startCollection(job.id)
            
            logger.info("Analysis job created successfully: ${job.id}")
            
            return VideoAnalysisResponse(
                jobId = job.id,
                status = job.status,
                streamUrl = stream.outputUrls["hls"],
                estimatedDuration = estimateProcessingDuration(job),
                createdAt = job.createdAt
            )
            
        } catch (e: Exception) {
            logger.error("Failed to create analysis job: ${job.id}", e)
            job.status = AnalysisJobStatus.FAILED
            job.updatedAt = Instant.now()
            job.errorMessage = e.message
            analysisJobRepository.save(job)
            throw VideoAnalysisException("Failed to create analysis job", e)
        }
    }

    override suspend fun getAnalysisResults(
        jobId: String,
        filter: AnalysisResultsFilter
    ): AnalysisResults {
        val job = analysisJobRepository.findById(jobId)
            ?: throw AnalysisJobNotFoundException(jobId)
        
        logger.info("Getting analysis results for job: $jobId")
        
        // Retrieve results from aggregator
        val rawResults = resultsAggregator.getResults(
            jobId = jobId,
            resultTypes = filter.resultTypes,
            timeRange = filter.timeRange,
            confidenceThreshold = filter.confidenceThreshold,
            limit = filter.limit
        )
        
        // Process and format results
        val formattedResults = formatAnalysisResults(rawResults, filter)
        
        // Generate summary statistics
        val summary = generateResultsSummary(formattedResults)
        
        return AnalysisResults(
            jobId = jobId,
            resultType = filter.resultTypes.firstOrNull() ?: "all",
            timeRange = filter.timeRange,
            results = formattedResults,
            summary = summary,
            metadata = mapOf(
                "total_frames_processed" to job.statistics?.framesProcessed,
                "processing_duration" to job.statistics?.processingDuration,
                "models_used" to job.analysisConfig.models.map { it.modelId }
            )
        )
    }

    override suspend fun processVideoFrame(request: VideoFrameProcessingRequest): VideoFrameProcessingResponse {
        logger.debug("Processing single video frame")
        
        try {
            // Decode frame
            val frame = decodeVideoFrame(request.frameData, request.format)
            
            // Load requested models
            val models = loadModels(request.models)
            
            // Process frame through vision engine
            val results = visionEngine.processFrame(
                frame = frame,
                models = models,
                options = request.processingOptions
            )
            
            // Format results
            val detections = results.map { result ->
                Detection(
                    className = result.className,
                    confidence = result.confidence,
                    boundingBox = result.boundingBox,
                    attributes = result.attributes,
                    trackingId = result.trackingId
                )
            }
            
            return VideoFrameProcessingResponse(
                frameNumber = request.frameNumber,
                timestamp = request.timestamp,
                detections = detections,
                processingTimeMs = results.processingTime,
                metadata = mapOf(
                    "frame_size" to "${frame.width}x${frame.height}",
                    "models_used" to models.map { it.id }
                )
            )
            
        } catch (e: Exception) {
            logger.error("Failed to process video frame", e)
            throw VideoProcessingException("Failed to process frame", e)
        }
    }

    override suspend fun getStreamAnalytics(
        streamId: String,
        timeRange: TimeRange,
        metrics: List<AnalyticsMetric>
    ): StreamAnalytics {
        val stream = streamManager.getStream(streamId)
            ?: throw VideoStreamNotFoundException(streamId)
        
        // Collect metrics from various sources
        val performanceMetrics = metricsCollector.getStreamMetrics(streamId, timeRange)
        val detectionMetrics = resultsAggregator.getDetectionMetrics(streamId, timeRange)
        val qualityMetrics = streamProcessor.getQualityMetrics(streamId, timeRange)
        
        return StreamAnalytics(
            streamId = streamId,
            timeRange = timeRange,
            metrics = combineMetrics(performanceMetrics, detectionMetrics, qualityMetrics),
            detections = detectionMetrics,
            qualityMetrics = qualityMetrics
        )
    }

    private suspend fun startRealTimeAnalysis(pipeline: ProcessingPipeline) {
        logger.info("Starting real-time analysis for pipeline: ${pipeline.id}")
        
        // Configure real-time processing parameters
        val realtimeConfig = RealtimeProcessingConfig(
            maxLatencyMs = 100,
            frameSkipTolerance = 0.1,
            bufferSize = 30,
            processingThreads = 4
        )
        
        // Start stream processing
        streamProcessor.startRealtimeProcessing(pipeline, realtimeConfig)
        
        // Initialize frame buffer
        frameBufferService.initializeBuffer(pipeline.id, realtimeConfig.bufferSize)
        
        // Start vision processing workers
        visionEngine.startRealtimeWorkers(pipeline.id, pipeline.models)
    }

    private suspend fun scheduleBatchAnalysis(pipeline: ProcessingPipeline) {
        logger.info("Scheduling batch analysis for pipeline: ${pipeline.id}")
        
        // Configure batch processing
        val batchConfig = BatchProcessingConfig(
            batchSize = 100,
            processingConcurrency = 8,
            checkpointInterval = Duration.ofMinutes(5),
            retryPolicy = RetryPolicy.exponentialBackoff(3, Duration.ofSeconds(1))
        )
        
        // Schedule batch job
        analysisOrchestrator.scheduleBatchJob(pipeline, batchConfig)
    }

    private fun validateAnalysisRequest(request: VideoAnalysisRequest) {
        if (request.name.isBlank()) {
            throw ValidationException("Analysis job name cannot be blank")
        }
        
        if (request.videoSource.url.isBlank()) {
            throw ValidationException("Video source URL is required")
        }
        
        if (request.analysisConfig.models.isEmpty()) {
            throw ValidationException("At least one analysis model must be specified")
        }
        
        // Validate models exist
        request.analysisConfig.models.forEach { modelConfig ->
            if (!modelRegistry.modelExists(modelConfig.modelId)) {
                throw ValidationException("Model not found: ${modelConfig.modelId}")
            }
        }
        
        // Validate frame sampling configuration
        val frameSampling = request.analysisConfig.frameSampling
        if (frameSampling.samplingRate <= 0) {
            throw ValidationException("Frame sampling rate must be positive")
        }
    }

    private fun loadModels(modelConfigs: List<ModelConfig>): List<LoadedModel> {
        return modelConfigs.map { config ->
            val model = modelRegistry.getModel(config.modelId)
            LoadedModel(
                id = model.id,
                name = model.name,
                instance = visionEngine.loadModel(model),
                config = config
            )
        }
    }
}

// Computer Vision Engine
@Component
class ComputerVisionEngine(
    private val tensorflowService: TensorFlowInferenceService,
    private val pytorchService: PyTorchInferenceService,
    private val onnxService: ONNXInferenceService,
    private val openvinoService: OpenVINOInferenceService,
    private val postProcessor: PostProcessor,
    private val trackingService: ObjectTrackingService
) {
    
    private val logger = LoggerFactory.getLogger(ComputerVisionEngine::class.java)
    private val modelInstances = ConcurrentHashMap<String, ModelInstance>()
    
    suspend fun processFrame(
        frame: VideoFrame,
        models: List<LoadedModel>,
        options: ProcessingOptions
    ): List<VisionResult> {
        
        val startTime = System.currentTimeMillis()
        val allResults = mutableListOf<VisionResult>()
        
        // Preprocess frame
        val preprocessedFrame = preprocessFrame(frame, options)
        
        // Run inference on each model
        models.forEach { model ->
            try {
                val rawResults = runInference(model, preprocessedFrame)
                val processedResults = postProcessor.process(rawResults, model.config)
                
                // Apply tracking if enabled
                val trackedResults = if (options.enableTracking) {
                    trackingService.updateTracking(model.id, processedResults)
                } else {
                    processedResults
                }
                
                allResults.addAll(trackedResults.map { result ->
                    VisionResult(
                        modelId = model.id,
                        className = result.className,
                        confidence = result.confidence,
                        boundingBox = result.boundingBox,
                        attributes = result.attributes,
                        trackingId = result.trackingId,
                        processingTime = System.currentTimeMillis() - startTime
                    )
                })
                
            } catch (e: Exception) {
                logger.error("Failed to process frame with model: ${model.id}", e)
            }
        }
        
        return allResults
    }
    
    suspend fun startRealtimeWorkers(pipelineId: String, models: List<LoadedModel>) {
        logger.info("Starting real-time processing workers for pipeline: $pipelineId")
        
        models.forEach { model ->
            // Create dedicated worker for each model
            CoroutineScope(Dispatchers.Default).launch {
                processRealtimeFrames(pipelineId, model)
            }
        }
    }
    
    private suspend fun processRealtimeFrames(pipelineId: String, model: LoadedModel) {
        val frameBuffer = frameBufferService.getBuffer(pipelineId)
        
        while (isActive) {
            try {
                val frame = frameBuffer.takeFrame() // Blocking call
                
                if (frame != null) {
                    val results = runInference(model, frame)
                    val processedResults = postProcessor.process(results, model.config)
                    
                    // Send results to aggregator
                    resultsAggregator.addResults(pipelineId, processedResults)
                }
                
            } catch (e: InterruptedException) {
                logger.info("Real-time worker interrupted for model: ${model.id}")
                break
            } catch (e: Exception) {
                logger.error("Error in real-time processing worker", e)
                delay(1000) // Brief pause before retrying
            }
        }
    }
    
    private suspend fun runInference(model: LoadedModel, frame: VideoFrame): List<RawInferenceResult> {
        return when (model.framework) {
            ModelFramework.TENSORFLOW -> tensorflowService.infer(model.instance, frame)
            ModelFramework.PYTORCH -> pytorchService.infer(model.instance, frame)
            ModelFramework.ONNX -> onnxService.infer(model.instance, frame)
            ModelFramework.OPENVINO -> openvinoService.infer(model.instance, frame)
        }
    }
    
    private fun preprocessFrame(frame: VideoFrame, options: ProcessingOptions): VideoFrame {
        var processedFrame = frame
        
        // Apply region of interest if specified
        options.regionOfInterest?.let { roi ->
            processedFrame = cropFrame(processedFrame, roi)
        }
        
        // Resize if needed
        options.targetResolution?.let { resolution ->
            processedFrame = resizeFrame(processedFrame, resolution)
        }
        
        // Normalize pixel values
        if (options.normalizePixels) {
            processedFrame = normalizeFrame(processedFrame)
        }
        
        return processedFrame
    }
    
    private fun cropFrame(frame: VideoFrame, roi: RegionOfInterest): VideoFrame {
        val x = (roi.x * frame.width).toInt()
        val y = (roi.y * frame.height).toInt()
        val width = (roi.width * frame.width).toInt()
        val height = (roi.height * frame.height).toInt()
        
        return frame.crop(x, y, width, height)
    }
}

// Stream processing service
@Component
class StreamProcessor(
    private val ffmpegService: FFmpegService,
    private val gstreamerService: GStreamerService,
    private val webrtcService: WebRTCService,
    private val frameExtractor: FrameExtractor,
    private val qualityAnalyzer: VideoQualityAnalyzer
) {
    
    private val activeStreams = ConcurrentHashMap<String, ProcessingStream>()
    
    suspend fun startRealtimeProcessing(
        pipeline: ProcessingPipeline,
        config: RealtimeProcessingConfig
    ) {
        logger.info("Starting real-time stream processing for pipeline: ${pipeline.id}")
        
        val stream = ProcessingStream(
            pipelineId = pipeline.id,
            source = pipeline.videoSource,
            config = config,
            frameBuffer = frameBufferService.getBuffer(pipeline.id)
        )
        
        activeStreams[pipeline.id] = stream
        
        // Start frame extraction
        CoroutineScope(Dispatchers.IO).launch {
            extractFramesFromStream(stream)
        }
        
        // Start quality monitoring
        CoroutineScope(Dispatchers.Default).launch {
            monitorStreamQuality(stream)
        }
    }
    
    private suspend fun extractFramesFromStream(stream: ProcessingStream) {
        try {
            when (stream.source.type) {
                VideoSourceType.RTMP -> extractFromRTMP(stream)
                VideoSourceType.WEBRTC -> extractFromWebRTC(stream)
                VideoSourceType.HLS -> extractFromHLS(stream)
                VideoSourceType.FILE -> extractFromFile(stream)
                else -> throw UnsupportedOperationException("Unsupported stream type: ${stream.source.type}")
            }
        } catch (e: Exception) {
            logger.error("Failed to extract frames from stream: ${stream.pipelineId}", e)
            throw StreamProcessingException("Frame extraction failed", e)
        }
    }
    
    private suspend fun extractFromRTMP(stream: ProcessingStream) {
        val extractorConfig = FrameExtractionConfig(
            inputUrl = stream.source.url,
            outputFormat = "rgb24",
            frameRate = stream.config.targetFrameRate,
            resolution = stream.config.targetResolution
        )
        
        frameExtractor.extractFrames(extractorConfig) { frame ->
            // Add frame to buffer
            stream.frameBuffer.addFrame(frame)
            
            // Update stream statistics
            stream.statistics.framesExtracted++
            stream.statistics.lastFrameTime = Instant.now()
        }
    }
    
    private suspend fun monitorStreamQuality(stream: ProcessingStream) {
        while (activeStreams.containsKey(stream.pipelineId)) {
            try {
                val qualityMetrics = qualityAnalyzer.analyzeStream(stream.source.url)
                
                stream.qualityMetrics = qualityMetrics
                
                // Check for quality issues
                if (qualityMetrics.bitrate < stream.config.minBitrate) {
                    logger.warn("Low bitrate detected for stream: ${stream.pipelineId}")
                    // Trigger quality adjustment
                    adjustStreamQuality(stream, qualityMetrics)
                }
                
                if (qualityMetrics.frameDropRate > stream.config.maxFrameDropRate) {
                    logger.warn("High frame drop rate for stream: ${stream.pipelineId}")
                    // Adjust processing parameters
                    adjustProcessingLoad(stream)
                }
                
                delay(Duration.ofSeconds(5))
                
            } catch (e: Exception) {
                logger.error("Error monitoring stream quality", e)
                delay(Duration.ofSeconds(10))
            }
        }
    }
    
    suspend fun getQualityMetrics(streamId: String, timeRange: TimeRange): QualityMetrics {
        val stream = activeStreams[streamId] ?: return QualityMetrics.empty()
        
        return stream.qualityMetrics ?: QualityMetrics.empty()
    }
}

Database Schema & Models

PostgreSQL Schema 

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-- Video analysis jobs table
CREATE TABLE video_analysis_jobs (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(255) NOT NULL,
    description TEXT,
    user_id UUID NOT NULL,
    status VARCHAR(50) NOT NULL DEFAULT 'creating',
    processing_mode VARCHAR(50) NOT NULL,
    priority VARCHAR(20) NOT NULL DEFAULT 'medium',
    
    -- Video source configuration
    video_source JSONB NOT NULL,
    
    -- Analysis configuration
    analysis_config JSONB NOT NULL,
    
    -- Processing information
    stream_id UUID,
    pipeline_id UUID,
    
    -- Statistics
    statistics JSONB DEFAULT '{}'::jsonb,
    
    -- Error information
    error_message TEXT,
    error_stack TEXT,
    
    -- Timing
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    started_at TIMESTAMP WITH TIME ZONE,
    completed_at TIMESTAMP WITH TIME ZONE,
    
    -- Progress tracking
    progress JSONB DEFAULT '{}'::jsonb,
    
    -- Tags for organization
    tags TEXT[] DEFAULT '{}',
    
    -- Constraints
    CONSTRAINT jobs_status_check CHECK (
        status IN ('creating', 'running', 'completed', 'failed', 'cancelled')
    ),
    CONSTRAINT jobs_mode_check CHECK (
        processing_mode IN ('real-time', 'batch', 'on-demand')
    ),
    CONSTRAINT jobs_priority_check CHECK (
        priority IN ('low', 'medium', 'high', 'critical')
    )
);

-- Indexes
CREATE INDEX idx_analysis_jobs_user_status ON video_analysis_jobs(user_id, status);
CREATE INDEX idx_analysis_jobs_status_created ON video_analysis_jobs(status, created_at DESC);
CREATE INDEX idx_analysis_jobs_mode ON video_analysis_jobs(processing_mode);
CREATE INDEX idx_analysis_jobs_tags ON video_analysis_jobs USING GIN(tags);

-- Video streams table
CREATE TABLE video_streams (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(255) NOT NULL,
    type VARCHAR(50) NOT NULL,
    status VARCHAR(50) NOT NULL DEFAULT 'inactive',
    
    -- Stream configuration
    source_url VARCHAR(1000) NOT NULL,
    output_urls JSONB DEFAULT '{}'::jsonb,
    
    -- Quality settings
    quality_profiles JSONB DEFAULT '[]'::jsonb,
    
    -- Stream health
    health_status VARCHAR(50) DEFAULT 'unknown',
    last_health_check TIMESTAMP WITH TIME ZONE,
    
    -- Performance metrics
    current_bitrate BIGINT,
    current_fps DECIMAL(5,2),
    viewer_count INTEGER DEFAULT 0,
    
    -- Timing
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    started_at TIMESTAMP WITH TIME ZONE,
    stopped_at TIMESTAMP WITH TIME ZONE,
    
    -- Constraints
    CONSTRAINT streams_type_check CHECK (
        type IN ('rtmp', 'webrtc', 'hls', 'dash', 'file', 'camera')
    ),
    CONSTRAINT streams_status_check CHECK (
        status IN ('active', 'inactive', 'error', 'starting', 'stopping')
    )
);

-- Analysis results table
CREATE TABLE analysis_results (
    id BIGSERIAL PRIMARY KEY,
    job_id UUID NOT NULL REFERENCES video_analysis_jobs(id) ON DELETE CASCADE,
    
    -- Detection details
    timestamp_seconds DECIMAL(10,3) NOT NULL,
    frame_number BIGINT NOT NULL,
    model_id VARCHAR(100) NOT NULL,
    
    -- Detection results
    detections JSONB NOT NULL,
    
    -- Metadata
    metadata JSONB DEFAULT '{}'::jsonb,
    
    -- Processing info
    processing_time_ms INTEGER,
    confidence_threshold DECIMAL(3,2),
    
    -- Timestamps
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    
    -- Partitioning key
    partition_date DATE DEFAULT CURRENT_DATE
);

-- Partitioning for analysis results (by date)
CREATE TABLE analysis_results_2024_01 PARTITION OF analysis_results
    FOR VALUES FROM ('2024-01-01') TO ('2024-02-01');

-- Continue partitioning for other months...

-- Indexes for analysis results
CREATE INDEX idx_analysis_results_job_time ON analysis_results(job_id, timestamp_seconds);
CREATE INDEX idx_analysis_results_model ON analysis_results(model_id);
CREATE INDEX idx_analysis_results_created ON analysis_results(created_at DESC);
CREATE INDEX idx_analysis_results_detections ON analysis_results USING GIN(detections);

-- Models table
CREATE TABLE vision_models (
    id VARCHAR(100) PRIMARY KEY,
    name VARCHAR(255) NOT NULL,
    category VARCHAR(100) NOT NULL,
    framework VARCHAR(50) NOT NULL,
    version VARCHAR(50) NOT NULL,
    
    -- Model information
    description TEXT,
    input_format JSONB NOT NULL,
    output_format JSONB NOT NULL,
    
    -- Model files and artifacts
    model_path VARCHAR(1000),
    config_path VARCHAR(1000),
    weights_path VARCHAR(1000),
    labels_path VARCHAR(1000),
    
    -- Performance characteristics
    model_size_mb DECIMAL(10,2),
    inference_time_ms DECIMAL(8,2),
    memory_usage_mb INTEGER,
    
    -- Hardware requirements
    supported_hardware TEXT[] DEFAULT '{}',
    min_gpu_memory_mb INTEGER,
    
    -- Deployment status
    deployment_status VARCHAR(50) DEFAULT 'available',
    deployed_instances INTEGER DEFAULT 0,
    
    -- Usage statistics
    usage_count BIGINT DEFAULT 0,
    last_used_at TIMESTAMP WITH TIME ZONE,
    
    -- Timestamps
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    
    -- Constraints
    CONSTRAINT models_category_check CHECK (
        category IN ('object_detection', 'face_recognition', 'scene_classification', 
                    'ocr', 'action_recognition', 'segmentation', 'pose_estimation')
    ),
    CONSTRAINT models_framework_check CHECK (
        framework IN ('tensorflow', 'pytorch', 'onnx', 'openvino')
    ),
    CONSTRAINT models_deployment_check CHECK (
        deployment_status IN ('available', 'deploying', 'deployed', 'error', 'deprecated')
    )
);

-- Model performance benchmarks table
CREATE TABLE model_benchmarks (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    model_id VARCHAR(100) NOT NULL REFERENCES vision_models(id) ON DELETE CASCADE,
    
    -- Benchmark configuration
    hardware_config JSONB NOT NULL,
    test_dataset VARCHAR(255),
    batch_size INTEGER,
    
    -- Performance metrics
    fps DECIMAL(8,2),
    latency_p50_ms DECIMAL(8,2),
    latency_p95_ms DECIMAL(8,2),
    latency_p99_ms DECIMAL(8,2),
    throughput_per_second DECIMAL(10,2),
    
    -- Accuracy metrics
    map_score DECIMAL(5,4),
    accuracy DECIMAL(5,4),
    precision DECIMAL(5,4),
    recall DECIMAL(5,4),
    f1_score DECIMAL(5,4),
    
    -- Resource usage
    cpu_usage_percent DECIMAL(5,2),
    memory_usage_mb INTEGER,
    gpu_usage_percent DECIMAL(5,2),
    gpu_memory_mb INTEGER,
    
    -- Timestamps
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

-- Stream analytics table (time-series data)
CREATE TABLE stream_analytics (
    id BIGSERIAL PRIMARY KEY,
    stream_id UUID REFERENCES video_streams(id) ON DELETE CASCADE,
    job_id UUID REFERENCES video_analysis_jobs(id) ON DELETE SET NULL,
    
    -- Metric details
    metric_name VARCHAR(100) NOT NULL,
    metric_value DECIMAL(12,4) NOT NULL,
    metric_unit VARCHAR(20),
    
    -- Dimensions
    dimensions JSONB DEFAULT '{}'::jsonb,
    
    -- Time bucket for aggregation
    time_bucket TIMESTAMP WITH TIME ZONE NOT NULL,
    
    -- Timestamp
    recorded_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

-- Partitioning for stream analytics (by day)
CREATE TABLE stream_analytics_2024_001 PARTITION OF stream_analytics
    FOR VALUES FROM ('2024-01-01') TO ('2024-01-02');

-- Continue partitioning...

-- Indexes for stream analytics
CREATE INDEX idx_stream_analytics_stream_time ON stream_analytics(stream_id, time_bucket DESC);
CREATE INDEX idx_stream_analytics_job_time ON stream_analytics(job_id, time_bucket DESC);
CREATE INDEX idx_stream_analytics_metric ON stream_analytics(metric_name);

-- Processing pipelines table
CREATE TABLE processing_pipelines (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    job_id UUID NOT NULL REFERENCES video_analysis_jobs(id) ON DELETE CASCADE,
    
    -- Pipeline configuration
    pipeline_config JSONB NOT NULL,
    
    -- Status and health
    status VARCHAR(50) NOT NULL DEFAULT 'creating',
    health_status VARCHAR(50) DEFAULT 'unknown',
    
    -- Performance metrics
    throughput_fps DECIMAL(8,2),
    latency_ms DECIMAL(8,2),
    error_rate DECIMAL(5,4),
    
    -- Resource allocation
    allocated_cpu_cores INTEGER,
    allocated_memory_mb INTEGER,
    allocated_gpu_count INTEGER,
    
    -- Timing
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    started_at TIMESTAMP WITH TIME ZONE,
    stopped_at TIMESTAMP WITH TIME ZONE,
    
    -- Constraints
    CONSTRAINT pipelines_status_check CHECK (
        status IN ('creating', 'starting', 'running', 'stopping', 'stopped', 'error')
    )
);

-- Frame buffers table
CREATE TABLE frame_buffers (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    pipeline_id UUID NOT NULL REFERENCES processing_pipelines(id) ON DELETE CASCADE,
    
    -- Buffer configuration
    max_size INTEGER NOT NULL DEFAULT 100,
    current_size INTEGER DEFAULT 0,
    
    -- Buffer statistics
    frames_added BIGINT DEFAULT 0,
    frames_consumed BIGINT DEFAULT 0,
    frames_dropped BIGINT DEFAULT 0,
    
    -- Performance metrics
    avg_buffer_utilization DECIMAL(5,4),
    max_buffer_utilization DECIMAL(5,4),
    buffer_overflow_count INTEGER DEFAULT 0,
    
    -- Timestamps
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    last_activity_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

-- Object tracking table
CREATE TABLE object_tracking (
    id BIGSERIAL PRIMARY KEY,
    job_id UUID NOT NULL REFERENCES video_analysis_jobs(id) ON DELETE CASCADE,
    
    -- Tracking information
    track_id VARCHAR(100) NOT NULL,
    object_class VARCHAR(100) NOT NULL,
    
    -- Tracking timeline
    first_seen_at DECIMAL(10,3) NOT NULL,
    last_seen_at DECIMAL(10,3) NOT NULL,
    duration_seconds DECIMAL(10,3),
    
    -- Tracking statistics
    detection_count INTEGER DEFAULT 1,
    avg_confidence DECIMAL(5,4),
    max_confidence DECIMAL(5,4),
    
    -- Bounding box trajectory
    trajectory JSONB DEFAULT '[]'::jsonb,
    
    -- Metadata
    metadata JSONB DEFAULT '{}'::jsonb,
    
    -- Timestamps
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    
    -- Unique tracking per job
    UNIQUE(job_id, track_id)
);

-- Indexes for object tracking
CREATE INDEX idx_tracking_job ON object_tracking(job_id);
CREATE INDEX idx_tracking_class ON object_tracking(object_class);
CREATE INDEX idx_tracking_duration ON object_tracking(duration_seconds DESC);
CREATE INDEX idx_tracking_timeline ON object_tracking(first_seen_at, last_seen_at);

-- Functions and triggers
CREATE OR REPLACE FUNCTION update_updated_at_column()
RETURNS TRIGGER AS $$
BEGIN
    NEW.updated_at = NOW();
    RETURN NEW;
END;
$$ language 'plpgsql';

-- Apply update triggers
CREATE TRIGGER update_analysis_jobs_updated_at BEFORE UPDATE
    ON video_analysis_jobs FOR EACH ROW EXECUTE FUNCTION update_updated_at_column();

CREATE TRIGGER update_streams_updated_at BEFORE UPDATE
    ON video_streams FOR EACH ROW EXECUTE FUNCTION update_updated_at_column();

CREATE TRIGGER update_models_updated_at BEFORE UPDATE
    ON vision_models FOR EACH ROW EXECUTE FUNCTION update_updated_at_column();

CREATE TRIGGER update_tracking_updated_at BEFORE UPDATE
    ON object_tracking FOR EACH ROW EXECUTE FUNCTION update_updated_at_column();

-- Views for common queries
CREATE VIEW job_performance_summary AS
SELECT 
    vaj.*,
    COUNT(ar.id) as total_detections,
    COUNT(DISTINCT ar.model_id) as models_used,
    AVG(ar.processing_time_ms) as avg_processing_time_ms,
    MAX(ar.timestamp_seconds) as max_timestamp_processed,
    COUNT(DISTINCT ot.track_id) as unique_objects_tracked
FROM video_analysis_jobs vaj
LEFT JOIN analysis_results ar ON vaj.id = ar.job_id
LEFT JOIN object_tracking ot ON vaj.id = ot.job_id
GROUP BY vaj.id;

CREATE VIEW model_usage_stats AS
SELECT 
    vm.*,
    COUNT(ar.id) as total_detections,
    COUNT(DISTINCT ar.job_id) as jobs_used_in,
    AVG(ar.processing_time_ms) as avg_processing_time,
    COUNT(mb.id) as benchmark_count,
    MAX(mb.fps) as best_fps_recorded
FROM vision_models vm
LEFT JOIN analysis_results ar ON vm.id = ar.model_id
LEFT JOIN model_benchmarks mb ON vm.id = mb.model_id
GROUP BY vm.id;

CREATE VIEW stream_health_dashboard AS
SELECT 
    vs.*,
    COUNT(vaj.id) as active_analysis_jobs,
    AVG(sa.metric_value) FILTER (WHERE sa.metric_name = 'fps') as avg_fps,
    AVG(sa.metric_value) FILTER (WHERE sa.metric_name = 'latency_ms') as avg_latency_ms,
    AVG(sa.metric_value) FILTER (WHERE sa.metric_name = 'bitrate') as avg_bitrate
FROM video_streams vs
LEFT JOIN video_analysis_jobs vaj ON vs.id = vaj.stream_id AND vaj.status = 'running'
LEFT JOIN stream_analytics sa ON vs.id = sa.stream_id 
    AND sa.time_bucket >= NOW() - INTERVAL '1 hour'
GROUP BY vs.id;

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// Video Analysis Job Entity
@Entity
@Table(name = "video_analysis_jobs")
@EntityListeners(AuditingEntityListener::class)
data class VideoAnalysisJobEntity(
    @Id
    @GeneratedValue(strategy = GenerationType.AUTO)
    val id: String = UUID.randomUUID().toString(),
    
    @Column(nullable = false)
    var name: String,
    
    @Column(columnDefinition = "TEXT")
    var description: String? = null,
    
    @Column(name = "user_id", nullable = false)
    var userId: UUID,
    
    @Enumerated(EnumType.STRING)
    var status: AnalysisJobStatus = AnalysisJobStatus.CREATING,
    
    @Enumerated(EnumType.STRING)
    @Column(name = "processing_mode")
    var processingMode: ProcessingMode,
    
    @Enumerated(EnumType.STRING)
    var priority: Priority = Priority.MEDIUM,
    
    @Type(JsonType::class)
    @Column(name = "video_source", columnDefinition = "jsonb")
    var videoSource: VideoSource,
    
    @Type(JsonType::class)
    @Column(name = "analysis_config", columnDefinition = "jsonb")
    var analysisConfig: AnalysisConfig,
    
    @Column(name = "stream_id")
    var streamId: UUID? = null,
    
    @Column(name = "pipeline_id")
    var pipelineId: UUID? = null,
    
    @Type(JsonType::class)
    @Column(columnDefinition = "jsonb")
    var statistics: JobStatistics? = null,
    
    @Column(name = "error_message", columnDefinition = "TEXT")
    var errorMessage: String? = null,
    
    @Column(name = "error_stack", columnDefinition = "TEXT")
    var errorStack: String? = null,
    
    @CreatedDate
    @Column(name = "created_at", nullable = false, updatable = false)
    var createdAt: Instant? = null,
    
    @LastModifiedDate
    @Column(name = "updated_at")
    var updatedAt: Instant? = null,
    
    @Column(name = "started_at")
    var startedAt: Instant? = null,
    
    @Column(name = "completed_at")
    var completedAt: Instant? = null,
    
    @Type(JsonType::class)
    @Column(columnDefinition = "jsonb")
    var progress: JobProgress? = null,
    
    @Type(ArrayType::class)
    @Column(columnDefinition = "text[]")
    var tags: Array<String> = emptyArray(),
    
    // Relationships
    @OneToMany(mappedBy = "job", cascade = [CascadeType.ALL], fetch = FetchType.LAZY)
    var results: MutableList<AnalysisResultEntity> = mutableListOf(),
    
    @OneToMany(mappedBy = "job", cascade = [CascadeType.ALL], fetch = FetchType.LAZY)
    var trackingData: MutableList<ObjectTrackingEntity> = mutableListOf(),
    
    @OneToMany(mappedBy = "job", cascade = [CascadeType.ALL], fetch = FetchType.LAZY)
    var analytics: MutableList<StreamAnalyticsEntity> = mutableListOf()
)

// Video Stream Entity
@Entity
@Table(name = "video_streams")
@EntityListeners(AuditingEntityListener::class)
data class VideoStreamEntity(
    @Id
    @GeneratedValue(strategy = GenerationType.AUTO)
    val id: UUID = UUID.randomUUID(),
    
    @Column(nullable = false)
    var name: String,
    
    @Enumerated(EnumType.STRING)
    var type: VideoStreamType,
    
    @Enumerated(EnumType.STRING)
    var status: StreamStatus = StreamStatus.INACTIVE,
    
    @Column(name = "source_url", nullable = false, length = 1000)
    var sourceUrl: String,
    
    @Type(JsonType::class)
    @Column(name = "output_urls", columnDefinition = "jsonb")
    var outputUrls: Map<String, String> = emptyMap(),
    
    @Type(JsonType::class)
    @Column(name = "quality_profiles", columnDefinition = "jsonb")
    var qualityProfiles: List<QualityProfile> = emptyList(),
    
    @Enumerated(EnumType.STRING)
    @Column(name = "health_status")
    var healthStatus: HealthStatus = HealthStatus.UNKNOWN,
    
    @Column(name = "last_health_check")
    var lastHealthCheck: Instant? = null,
    
    @Column(name = "current_bitrate")
    var currentBitrate: Long? = null,
    
    @Column(name = "current_fps", precision = 5, scale = 2)
    var currentFps: BigDecimal? = null,
    
    @Column(name = "viewer_count")
    var viewerCount: Int = 0,
    
    @CreatedDate
    @Column(name = "created_at", nullable = false, updatable = false)
    var createdAt: Instant? = null,
    
    @LastModifiedDate
    @Column(name = "updated_at")
    var updatedAt: Instant? = null,
    
    @Column(name = "started_at")
    var startedAt: Instant? = null,
    
    @Column(name = "stopped_at")
    var stoppedAt: Instant? = null,
    
    // Relationships
    @OneToMany(mappedBy = "stream", cascade = [CascadeType.ALL], fetch = FetchType.LAZY)
    var analytics: MutableList<StreamAnalyticsEntity> = mutableListOf()
)

// Analysis Result Entity
@Entity
@Table(name = "analysis_results")
data class AnalysisResultEntity(
    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    val id: Long? = null,
    
    @ManyToOne(fetch = FetchType.LAZY)
    @JoinColumn(name = "job_id", nullable = false)
    var job: VideoAnalysisJobEntity,
    
    @Column(name = "timestamp_seconds", nullable = false, precision = 10, scale = 3)
    var timestampSeconds: BigDecimal,
    
    @Column(name = "frame_number", nullable = false)
    var frameNumber: Long,
    
    @Column(name = "model_id", nullable = false, length = 100)
    var modelId: String,
    
    @Type(JsonType::class)
    @Column(columnDefinition = "jsonb")
    var detections: List<Detection>,
    
    @Type(JsonType::class)
    @Column(columnDefinition = "jsonb")
    var metadata: Map<String, Any> = emptyMap(),
    
    @Column(name = "processing_time_ms")
    var processingTimeMs: Int? = null,
    
    @Column(name = "confidence_threshold", precision = 3, scale = 2)
    var confidenceThreshold: BigDecimal? = null,
    
    @CreatedDate
    @Column(name = "created_at", nullable = false, updatable = false)
    var createdAt: Instant? = null,
    
    @Column(name = "partition_date")
    var partitionDate: LocalDate = LocalDate.now()
)

// Vision Model Entity
@Entity
@Table(name = "vision_models")
@EntityListeners(AuditingEntityListener::class)
data class VisionModelEntity(
    @Id
    val id: String,
    
    @Column(nullable = false)
    var name: String,
    
    @Column(nullable = false)
    var category: String,
    
    @Enumerated(EnumType.STRING)
    var framework: ModelFramework,
    
    @Column(nullable = false)
    var version: String,
    
    @Column(columnDefinition = "TEXT")
    var description: String? = null,
    
    @Type(JsonType::class)
    @Column(name = "input_format", columnDefinition = "jsonb")
    var inputFormat: InputFormat,
    
    @Type(JsonType::class)
    @Column(name = "output_format", columnDefinition = "jsonb")
    var outputFormat: OutputFormat,
    
    @Column(name = "model_path", length = 1000)
    var modelPath: String? = null,
    
    @Column(name = "config_path", length = 1000)
    var configPath: String? = null,
    
    @Column(name = "weights_path", length = 1000)
    var weightsPath: String? = null,
    
    @Column(name = "labels_path", length = 1000)
    var labelsPath: String? = null,
    
    @Column(name = "model_size_mb", precision = 10, scale = 2)
    var modelSizeMb: BigDecimal? = null,
    
    @Column(name = "inference_time_ms", precision = 8, scale = 2)
    var inferenceTimeMs: BigDecimal? = null,
    
    @Column(name = "memory_usage_mb")
    var memoryUsageMb: Int? = null,
    
    @Type(ArrayType::class)
    @Column(name = "supported_hardware", columnDefinition = "text[]")
    var supportedHardware: Array<String> = emptyArray(),
    
    @Column(name = "min_gpu_memory_mb")
    var minGpuMemoryMb: Int? = null,
    
    @Enumerated(EnumType.STRING)
    @Column(name = "deployment_status")
    var deploymentStatus: ModelDeploymentStatus = ModelDeploymentStatus.AVAILABLE,
    
    @Column(name = "deployed_instances")
    var deployedInstances: Int = 0,
    
    @Column(name = "usage_count")
    var usageCount: Long = 0,
    
    @Column(name = "last_used_at")
    var lastUsedAt: Instant? = null,
    
    @CreatedDate
    @Column(name = "created_at", nullable = false, updatable = false)
    var createdAt: Instant? = null,
    
    @LastModifiedDate
    @Column(name = "updated_at")
    var updatedAt: Instant? = null,
    
    // Relationships
    @OneToMany(mappedBy = "model", cascade = [CascadeType.ALL], fetch = FetchType.LAZY)
    var benchmarks: MutableList<ModelBenchmarkEntity> = mutableListOf()
)

// Object Tracking Entity
@Entity
@Table(name = "object_tracking")
@EntityListeners(AuditingEntityListener::class)
data class ObjectTrackingEntity(
    @Id
    @GeneratedValue(strategy = GenerationType.IDENTITY)
    val id: Long? = null,
    
    @ManyToOne(fetch = FetchType.LAZY)
    @JoinColumn(name = "job_id", nullable = false)
    var job: VideoAnalysisJobEntity,
    
    @Column(name = "track_id", nullable = false, length = 100)
    var trackId: String,
    
    @Column(name = "object_class", nullable = false, length = 100)
    var objectClass: String,
    
    @Column(name = "first_seen_at", nullable = false, precision = 10, scale = 3)
    var firstSeenAt: BigDecimal,
    
    @Column(name = "last_seen_at", nullable = false, precision = 10, scale = 3)
    var lastSeenAt: BigDecimal,
    
    @Column(name = "duration_seconds", precision = 10, scale = 3)
    var durationSeconds: BigDecimal? = null,
    
    @Column(name = "detection_count")
    var detectionCount: Int = 1,
    
    @Column(name = "avg_confidence", precision = 5, scale = 4)
    var avgConfidence: BigDecimal? = null,
    
    @Column(name = "max_confidence", precision = 5, scale = 4)
    var maxConfidence: BigDecimal? = null,
    
    @Type(JsonType::class)
    @Column(columnDefinition = "jsonb")
    var trajectory: List<TrajectoryPoint> = emptyList(),
    
    @Type(JsonType::class)
    @Column(columnDefinition = "jsonb")
    var metadata: Map<String, Any> = emptyMap(),
    
    @CreatedDate
    @Column(name = "created_at", nullable = false, updatable = false)
    var createdAt: Instant? = null,
    
    @LastModifiedDate
    @Column(name = "updated_at")
    var updatedAt: Instant? = null
)

// Enums
enum class AnalysisJobStatus {
    CREATING, RUNNING, COMPLETED, FAILED, CANCELLED
}

enum class ProcessingMode {
    REAL_TIME, BATCH, ON_DEMAND
}

enum class VideoStreamType {
    RTMP, WEBRTC, HLS, DASH, FILE, CAMERA
}

enum class StreamStatus {
    ACTIVE, INACTIVE, ERROR, STARTING, STOPPING
}

enum class ModelFramework {
    TENSORFLOW, PYTORCH, ONNX, OPENVINO
}

enum class ModelDeploymentStatus {
    AVAILABLE, DEPLOYING, DEPLOYED, ERROR, DEPRECATED
}

enum class Priority {
    LOW, MEDIUM, HIGH, CRITICAL
}

enum class HealthStatus {
    HEALTHY, DEGRADED, UNHEALTHY, UNKNOWN
}

// Data classes for complex types
data class VideoSource(
    val type: VideoSourceType,
    val url: String,
    val credentials: Credentials? = null,
    val qualitySettings: QualitySettings? = null
)

data class AnalysisConfig(
    val models: List<ModelConfig>,
    val frameSampling: FrameSamplingConfig,
    val outputConfig: OutputConfig,
    val performanceConfig: PerformanceConfig
)

data class Detection(
    val className: String,
    val confidence: Double,
    val boundingBox: BoundingBox,
    val attributes: Map<String, Any> = emptyMap(),
    val trackingId: String? = null
)

data class BoundingBox(
    val x: Double,
    val y: Double,
    val width: Double,
    val height: Double
)

data class JobStatistics(
    val framesProcessed: Long = 0,
    val detectionsFound: Long = 0,
    val processingDuration: Duration? = null,
    val avgProcessingTimeMs: Double = 0.0,
    val errorCount: Int = 0
)

data class QualityProfile(
    val name: String,
    val width: Int,
    val height: Int,
    val bitrate: Int,
    val fps: Int,
    val codec: String
)

data class TrajectoryPoint(
    val timestamp: Double,
    val boundingBox: BoundingBox,
    val confidence: Double
)

Message Queue Patterns

Apache Kafka Integration for Video Intelligence 

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// Kafka configuration for video intelligence
@Configuration
@EnableKafka
class VideoIntelligenceKafkaConfig {
    
    @Value("\${spring.kafka.bootstrap-servers}")
    private lateinit var bootstrapServers: String
    
    @Bean
    fun videoProducerFactory(): ProducerFactory<String, Any> {
        return DefaultKafkaProducerFactory(videoProducerConfigs())
    }
    
    @Bean
    fun videoProducerConfigs(): Map<String, Any> {
        return mapOf(
            ProducerConfig.BOOTSTRAP_SERVERS_CONFIG to bootstrapServers,
            ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG to StringSerializer::class.java,
            ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG to JsonSerializer::class.java,
            ProducerConfig.ACKS_CONFIG to "all",
            ProducerConfig.RETRIES_CONFIG to 3,
            ProducerConfig.BATCH_SIZE_CONFIG to 32768, // Larger batch for video data
            ProducerConfig.LINGER_MS_CONFIG to 10,
            ProducerConfig.COMPRESSION_TYPE_CONFIG to "lz4", // Fast compression for real-time
            ProducerConfig.BUFFER_MEMORY_CONFIG to 67108864L, // 64MB buffer
            ProducerConfig.MAX_REQUEST_SIZE_CONFIG to 10485760 // 10MB max request
        )
    }
    
    @Bean
    fun videoKafkaTemplate(): KafkaTemplate<String, Any> {
        return KafkaTemplate(videoProducerFactory()).apply {
            setDefaultTopic("video-intelligence.events")
        }
    }
    
    // High-throughput consumer for real-time processing
    @Bean
    fun realtimeConsumerFactory(): ConsumerFactory<String, Any> {
        return DefaultKafkaConsumerFactory(realtimeConsumerConfigs())
    }
    
    @Bean
    fun realtimeConsumerConfigs(): Map<String, Any> {
        return mapOf(
            ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG to bootstrapServers,
            ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG to StringDeserializer::class.java,
            ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG to JsonDeserializer::class.java,
            ConsumerConfig.GROUP_ID_CONFIG to "video-realtime-processors",
            ConsumerConfig.AUTO_OFFSET_RESET_CONFIG to "latest",
            ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG to false,
            ConsumerConfig.MAX_POLL_RECORDS_CONFIG to 500,
            ConsumerConfig.FETCH_MIN_BYTES_CONFIG to 1048576, // 1MB
            ConsumerConfig.FETCH_MAX_WAIT_MS_CONFIG to 100,
            ConsumerConfig.SESSION_TIMEOUT_MS_CONFIG to 30000,
            ConsumerConfig.HEARTBEAT_INTERVAL_MS_CONFIG to 10000
        )
    }
}

// Video intelligence event publisher
@Component
class VideoIntelligenceEventPublisher(
    private val kafkaTemplate: KafkaTemplate<String, Any>
) {
    
    private val logger = LoggerFactory.getLogger(VideoIntelligenceEventPublisher::class.java)
    
    suspend fun publishAnalysisJobCreated(job: VideoAnalysisJobEntity) {
        val event = AnalysisJobCreatedEvent(
            jobId = job.id,
            name = job.name,
            processingMode = job.processingMode,
            priority = job.priority,
            videoSource = job.videoSource,
            analysisConfig = job.analysisConfig,
            userId = job.userId.toString(),
            timestamp = job.createdAt ?: Instant.now()
        )
        
        kafkaTemplate.send("video.analysis.job.created", job.id, event)
            .addCallback(
                { result ->
                    logger.debug("Published analysis job created event: ${result?.recordMetadata}")
                },
                { failure ->
                    logger.error("Failed to publish analysis job created event", failure)
                }
            )
    }
    
    suspend fun publishDetectionResults(
        jobId: String,
        results: List<AnalysisResultEntity>
    ) {
        val event = DetectionResultsEvent(
            jobId = jobId,
            results = results.map { result ->
                DetectionResultData(
                    timestamp = result.timestampSeconds.toDouble(),
                    frameNumber = result.frameNumber,
                    modelId = result.modelId,
                    detections = result.detections,
                    processingTimeMs = result.processingTimeMs
                )
            },
            timestamp = Instant.now()
        )
        
        // Use job ID for partitioning to maintain order
        kafkaTemplate.send("video.detections.results", jobId, event)
    }
    
    suspend fun publishRealTimeDetection(detection: RealTimeDetection) {
        val event = RealTimeDetectionEvent(
            streamId = detection.streamId,
            jobId = detection.jobId,
            detection = detection,
            timestamp = Instant.now()
        )
        
        // Send to real-time topic with low latency
        kafkaTemplate.send("video.detections.realtime", detection.streamId, event)
    }
    
    suspend fun publishStreamStatus(stream: VideoStreamEntity, previousStatus: StreamStatus) {
        val event = StreamStatusChangeEvent(
            streamId = stream.id.toString(),
            name = stream.name,
            previousStatus = previousStatus,
            currentStatus = stream.status,
            healthStatus = stream.healthStatus,
            timestamp = Instant.now()
        )
        
        kafkaTemplate.send("video.stream.status", stream.id.toString(), event)
    }
    
    suspend fun publishFrameProcessed(
        jobId: String,
        frameNumber: Long,
        processingTimeMs: Int,
        detectionCount: Int
    ) {
        val event = FrameProcessedEvent(
            jobId = jobId,
            frameNumber = frameNumber,
            processingTimeMs = processingTimeMs,
            detectionCount = detectionCount,
            timestamp = Instant.now()
        )
        
        kafkaTemplate.send("video.frame.processed", jobId, event)
    }
    
    suspend fun publishPerformanceMetrics(
        jobId: String?,
        streamId: String?,
        metrics: PerformanceMetrics
    ) {
        val event = PerformanceMetricsEvent(
            jobId = jobId,
            streamId = streamId,
            metrics = metrics,
            timestamp = Instant.now()
        )
        
        val key = jobId ?: streamId ?: "global"
        kafkaTemplate.send("video.performance.metrics", key, event)
    }
}

// Real-time processing with Kafka Streams
@Component
class VideoProcessingStreams(
    private val visionEngine: ComputerVisionEngine,
    private val resultsAggregator: ResultsAggregator,
    private val alertService: AlertService,
    private val analyticsService: VideoAnalyticsService
) {
    
    @Bean
    fun buildVideoProcessingTopology(): Topology {
        val builder = StreamsBuilder()
        
        // Real-time detection stream
        val detectionStream = builder.stream<String, String>("video.detections.realtime")
        
        // Aggregate detections for analytics
        val detectionAggregates = detectionStream
            .mapValues { value -> parseDetectionEvent(value) }
            .groupByKey()
            .windowedBy(TimeWindows.of(Duration.ofSeconds(30)))
            .aggregate(
                { DetectionAggregation() },
                { _, detection, agg -> agg.add(detection) },
                Materialized.`as`<String, DetectionAggregation, WindowStore<Bytes, ByteArray>>("detection-aggregates")
            )
        
        // Alert generation for anomalies
        val alertStream = detectionAggregates.toStream()
            .mapValues { windowedAgg ->
                val agg = windowedAgg.value
                if (shouldTriggerAlert(agg)) {
                    generateAlert(agg)
                } else null
            }
            .filter { _, alert -> alert != null }
        
        alertStream.foreach { streamId, alert ->
            alertService.sendAlert(alert!!)
        }
        
        // Performance monitoring
        val performanceStream = builder.stream<String, String>("video.performance.metrics")
            .mapValues { value -> parsePerformanceEvent(value) }
            .groupByKey()
            .windowedBy(TimeWindows.of(Duration.ofMinutes(1)))
            .aggregate(
                { PerformanceAggregation() },
                { _, metrics, agg -> agg.add(metrics) },
                Materialized.`as`<String, PerformanceAggregation, WindowStore<Bytes, ByteArray>>("performance-aggregates")
            )
        
        // Real-time analytics updates
        performanceStream.toStream().foreach { jobId, windowedAgg ->
            analyticsService.updateRealTimeMetrics(jobId.key(), windowedAgg.value)
        }
        
        // Frame processing monitoring
        val frameStream = builder.stream<String, String>("video.frame.processed")
            .mapValues { value -> parseFrameEvent(value) }
        
        // Track processing rate and latency
        val processingMetrics = frameStream
            .groupByKey()
            .windowedBy(TimeWindows.of(Duration.ofSeconds(10)))
            .aggregate(
                { FrameProcessingMetrics() },
                { _, frame, metrics -> metrics.addFrame(frame) },
                Materialized.`as`<String, FrameProcessingMetrics, WindowStore<Bytes, ByteArray>>("frame-metrics")
            )
        
        // Detect processing bottlenecks
        processingMetrics.toStream()
            .filter { _, windowedMetrics -> 
                windowedMetrics.value.avgProcessingTime > 1000 // > 1 second per frame
            }
            .foreach { jobId, metrics ->
                alertService.sendProcessingDelayAlert(jobId.key(), metrics.value)
            }
        
        return builder.build()
    }
    
    private fun parseDetectionEvent(json: String): RealTimeDetection {
        // Parse JSON to detection object
        return objectMapper.readValue(json, RealTimeDetection::class.java)
    }
    
    private fun shouldTriggerAlert(agg: DetectionAggregation): Boolean {
        // Define alert conditions
        return agg.detectionCount > 100 || // High activity
               agg.avgConfidence < 0.3 ||   // Low confidence
               agg.uniqueClasses.contains("weapon") // Security threat
    }
    
    private fun generateAlert(agg: DetectionAggregation): VideoAlert {
        return VideoAlert(
            type = determineAlertType(agg),
            severity = determineAlertSeverity(agg),
            message = buildAlertMessage(agg),
            data = agg
        )
    }
}

// High-throughput frame processing consumer
@Component
class RealtimeFrameProcessor {
    
    @KafkaListener(
        topics = ["video.frames.raw"],
        groupId = "realtime-processors",
        concurrency = "4",
        containerFactory = "realtimeConsumerFactory"
    )
    fun processFrame(
        @Payload frameData: ByteArray,
        @Header("stream_id") streamId: String,
        @Header("frame_number") frameNumber: Long,
        @Header("timestamp") timestamp: Double
    ) {
        try {
            // Decode frame
            val frame = decodeFrame(frameData)
            
            // Process with vision models
            val results = visionEngine.processFrameRealtime(frame)
            
            // Publish results immediately
            results.forEach { result ->
                val detection = RealTimeDetection(
                    streamId = streamId,
                    frameNumber = frameNumber,
                    timestamp = timestamp,
                    modelId = result.modelId,
                    detections = result.detections
                )
                
                eventPublisher.publishRealTimeDetection(detection)
            }
            
        } catch (e: Exception) {
            logger.error("Failed to process frame: $streamId:$frameNumber", e)
        }
    }
}

// Event DTOs
data class AnalysisJobCreatedEvent(
    val jobId: String,
    val name: String,
    val processingMode: ProcessingMode,
    val priority: Priority,
    val videoSource: VideoSource,
    val analysisConfig: AnalysisConfig,
    val userId: String,
    val timestamp: Instant
)

data class DetectionResultsEvent(
    val jobId: String,
    val results: List<DetectionResultData>,
    val timestamp: Instant
)

data class RealTimeDetectionEvent(
    val streamId: String,
    val jobId: String?,
    val detection: RealTimeDetection,
    val timestamp: Instant
)

data class StreamStatusChangeEvent(
    val streamId: String,
    val name: String,
    val previousStatus: StreamStatus,
    val currentStatus: StreamStatus,
    val healthStatus: HealthStatus,
    val timestamp: Instant
)

data class FrameProcessedEvent(
    val jobId: String,
    val frameNumber: Long,
    val processingTimeMs: Int,
    val detectionCount: Int,
    val timestamp: Instant
)

data class RealTimeDetection(
    val streamId: String,
    val jobId: String? = null,
    val frameNumber: Long,
    val timestamp: Double,
    val modelId: String,
    val detections: List<Detection>
)

// Aggregation classes
data class DetectionAggregation(
    var detectionCount: Int = 0,
    var avgConfidence: Double = 0.0,
    val uniqueClasses: MutableSet<String> = mutableSetOf(),
    val detectionsByClass: MutableMap<String, Int> = mutableMapOf()
) {
    fun add(detection: RealTimeDetection): DetectionAggregation {
        detection.detections.forEach { det ->
            detectionCount++
            uniqueClasses.add(det.className)
            detectionsByClass[det.className] = detectionsByClass.getOrDefault(det.className, 0) + 1
            
            // Update rolling average confidence
            avgConfidence = (avgConfidence * (detectionCount - 1) + det.confidence) / detectionCount
        }
        return this
    }
}

data class FrameProcessingMetrics(
    var frameCount: Int = 0,
    var totalProcessingTime: Long = 0,
    var avgProcessingTime: Double = 0.0,
    var maxProcessingTime: Int = 0
) {
    fun addFrame(frame: FrameProcessedEvent): FrameProcessingMetrics {
        frameCount++
        totalProcessingTime += frame.processingTimeMs
        avgProcessingTime = totalProcessingTime.toDouble() / frameCount
        maxProcessingTime = maxOf(maxProcessingTime, frame.processingTimeMs)
        return this
    }
}

Performance & Scaling

GPU Resource Management 

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// GPU resource manager for video processing
@Component
class GPUResourceManager(
    private val gpuMonitoringService: GPUMonitoringService,
    private val modelLoadBalancer: ModelLoadBalancer
) {
    
    private val gpuResources = ConcurrentHashMap<Int, GPUResource>()
    private val modelAssignments = ConcurrentHashMap<String, Int>() // modelId -> gpuId
    
    @PostConstruct
    fun initializeGPUResources() {
        val availableGPUs = detectAvailableGPUs()
        
        availableGPUs.forEach { gpuId ->
            val resource = GPUResource(
                id = gpuId,
                totalMemoryMB = getGPUMemory(gpuId),
                utilizationThreshold = 0.8,
                maxConcurrentModels = 4
            )
            gpuResources[gpuId] = resource
            
            // Start monitoring
            startGPUMonitoring(gpuId)
        }
        
        logger.info("Initialized ${gpuResources.size} GPU resources")
    }
    
    suspend fun allocateGPU(modelId: String, memoryRequirementMB: Int): Int? {
        // Find best GPU for model
        val suitableGPU = findBestGPU(memoryRequirementMB)
        
        if (suitableGPU != null) {
            val resource = gpuResources[suitableGPU]!!
            resource.allocateMemory(memoryRequirementMB)
            resource.assignModel(modelId)
            modelAssignments[modelId] = suitableGPU
            
            logger.info("Allocated GPU $suitableGPU for model $modelId")
            return suitableGPU
        }
        
        logger.warn("No suitable GPU found for model $modelId (requires ${memoryRequirementMB}MB)")
        return null
    }
    
    suspend fun releaseGPU(modelId: String) {
        val gpuId = modelAssignments.remove(modelId)
        if (gpuId != null) {
            val resource = gpuResources[gpuId]!!
            resource.releaseModel(modelId)
            logger.info("Released GPU $gpuId from model $modelId")
        }
    }
    
    private fun findBestGPU(memoryRequirementMB: Int): Int? {
        return gpuResources.values
            .filter { it.canAllocate(memoryRequirementMB) }
            .minByOrNull { it.utilizationPercent }
            ?.id
    }
    
    private fun startGPUMonitoring(gpuId: Int) {
        CoroutineScope(Dispatchers.Default).launch {
            while (isActive) {
                try {
                    val utilization = gpuMonitoringService.getGPUUtilization(gpuId)
                    val memoryUsage = gpuMonitoringService.getGPUMemoryUsage(gpuId)
                    
                    val resource = gpuResources[gpuId]!!
                    resource.updateMetrics(utilization, memoryUsage)
                    
                    // Check for overutilization
                    if (utilization > resource.utilizationThreshold) {
                        handleOverutilization(gpuId, utilization)
                    }
                    
                    delay(Duration.ofSeconds(5))
                    
                } catch (e: Exception) {
                    logger.error("Error monitoring GPU $gpuId", e)
                    delay(Duration.ofSeconds(10))
                }
            }
        }
    }
    
    private suspend fun handleOverutilization(gpuId: Int, utilization: Double) {
        logger.warn("GPU $gpuId overutilized: ${utilization * 100}%")
        
        // Try to rebalance models
        val resource = gpuResources[gpuId]!!
        val modelsToMove = resource.assignedModels.take(1) // Move one model
        
        modelsToMove.forEach { modelId ->
            val alternativeGPU = findAlternativeGPU(modelId, gpuId)
            if (alternativeGPU != null) {
                modelLoadBalancer.migrateModel(modelId, gpuId, alternativeGPU)
            }
        }
    }
}

// Distributed processing with Kubernetes
@Component
class KubernetesVideoProcessorScaler(
    private val kubernetesClient: KubernetesClient,
    private val metricsService: MetricsService
) {
    
    suspend fun scaleProcessors(jobId: String, targetThroughput: Double) {
        val currentMetrics = metricsService.getProcessingMetrics(jobId)
        val currentThroughput = currentMetrics.framesPerSecond
        
        if (currentThroughput < targetThroughput * 0.8) {
            // Need to scale up
            val scaleFactor = (targetThroughput / currentThroughput).coerceAtMost(2.0)
            scaleUp(jobId, scaleFactor.toInt())
        } else if (currentThroughput > targetThroughput * 1.2) {
            // Can scale down
            scaleDown(jobId)
        }
    }
    
    private suspend fun scaleUp(jobId: String, replicas: Int) {
        val deployment = createProcessorDeployment(jobId, replicas)
        
        kubernetesClient.apps().deployments()
            .inNamespace("video-processing")
            .create(deployment)
        
        logger.info("Scaled up video processors for job $jobId to $replicas replicas")
    }
    
    private fun createProcessorDeployment(jobId: String, replicas: Int): Deployment {
        return Deployment().apply {
            metadata = ObjectMeta().apply {
                name = "video-processor-$jobId"
                namespace = "video-processing"
                labels = mapOf(
                    "app" to "video-processor",
                    "job-id" to jobId,
                    "component" to "realtime-processor"
                )
            }
            
            spec = DeploymentSpec().apply {
                this.replicas = replicas
                selector = LabelSelector().apply {
                    matchLabels = mapOf("job-id" to jobId)
                }
                
                template = PodTemplateSpec().apply {
                    metadata = ObjectMeta().apply {
                        labels = mapOf(
                            "app" to "video-processor",
                            "job-id" to jobId
                        )
                    }
                    
                    spec = PodSpec().apply {
                        containers = listOf(
                            Container().apply {
                                name = "processor"
                                image = "aimatrix/video-processor:latest"
                                
                                env = listOf(
                                    EnvVar("JOB_ID", jobId, null),
                                    EnvVar("KAFKA_BROKERS", "kafka:9092", null),
                                    EnvVar("GPU_ENABLED", "true", null)
                                )
                                
                                resources = io.fabric8.kubernetes.api.model.ResourceRequirements().apply {
                                    requests = mapOf(
                                        "cpu" to Quantity("2"),
                                        "memory" to Quantity("4Gi"),
                                        "nvidia.com/gpu" to Quantity("1")
                                    )
                                    limits = mapOf(
                                        "cpu" to Quantity("4"),
                                        "memory" to Quantity("8Gi"),
                                        "nvidia.com/gpu" to Quantity("1")
                                    )
                                }
                                
                                livenessProbe = Probe().apply {
                                    httpGet = HTTPGetAction().apply {
                                        path = "/health"
                                        port = IntOrString(8080)
                                    }
                                    initialDelaySeconds = 30
                                    periodSeconds = 30
                                }
                                
                                readinessProbe = Probe().apply {
                                    httpGet = HTTPGetAction().apply {
                                        path = "/ready"
                                        port = IntOrString(8080)
                                    }
                                    initialDelaySeconds = 15
                                    periodSeconds = 10
                                }
                            }
                        )
                        
                        nodeSelector = mapOf(
                            "gpu" to "nvidia",
                            "instance-type" to "gpu-optimized"
                        )
                        
                        tolerations = listOf(
                            Toleration().apply {
                                key = "nvidia.com/gpu"
                                operator = "Exists"
                                effect = "NoSchedule"
                            }
                        )
                    }
                }
            }
        }
    }
}

Edge Processing Optimization 

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// Edge computing optimization for video processing
@Component
class EdgeProcessingOptimizer(
    private val modelOptimizer: ModelOptimizer,
    private val edgeDeploymentService: EdgeDeploymentService,
    private val bandwidthMonitor: BandwidthMonitor
) {
    
    suspend fun optimizeForEdge(
        jobId: String,
        edgeCapabilities: EdgeCapabilities
    ): EdgeOptimizationResult {
        
        val job = analysisJobRepository.findById(jobId) 
            ?: throw AnalysisJobNotFoundException(jobId)
        
        // Analyze models for edge compatibility
        val optimizedModels = job.analysisConfig.models.map { modelConfig ->
            optimizeModelForEdge(modelConfig, edgeCapabilities)
        }
        
        // Determine processing strategy
        val strategy = determineEdgeStrategy(optimizedModels, edgeCapabilities)
        
        // Configure edge deployment
        val edgeConfig = createEdgeConfig(job, optimizedModels, strategy)
        
        return EdgeOptimizationResult(
            optimizedModels = optimizedModels,
            strategy = strategy,
            edgeConfig = edgeConfig,
            expectedPerformance = estimateEdgePerformance(edgeConfig)
        )
    }
    
    private suspend fun optimizeModelForEdge(
        modelConfig: ModelConfig,
        capabilities: EdgeCapabilities
    ): OptimizedModelConfig {
        
        val model = modelRegistry.getModel(modelConfig.modelId)
        
        // Model optimization options
        val optimizations = mutableListOf<ModelOptimization>()
        
        // Quantization for smaller models
        if (capabilities.supportsQuantization && model.modelSizeMb!! > 100) {
            optimizations.add(ModelOptimization.INT8_QUANTIZATION)
        }
        
        // Pruning for faster inference
        if (capabilities.limitedComputePower) {
            optimizations.add(ModelOptimization.WEIGHT_PRUNING)
        }
        
        // Knowledge distillation for very constrained devices
        if (capabilities.memoryMB < 2000) {
            optimizations.add(ModelOptimization.KNOWLEDGE_DISTILLATION)
        }
        
        // Apply optimizations
        val optimizedModel = modelOptimizer.optimize(model, optimizations)
        
        return OptimizedModelConfig(
            originalModelId = model.id,
            optimizedModelId = optimizedModel.id,
            config = modelConfig,
            optimizations = optimizations,
            sizeBefore = model.modelSizeMb!!,
            sizeAfter = optimizedModel.modelSizeMb!!,
            performanceImprovement = estimatePerformanceImprovement(optimizations)
        )
    }
    
    private fun determineEdgeStrategy(
        models: List<OptimizedModelConfig>,
        capabilities: EdgeCapabilities
    ): EdgeProcessingStrategy {
        
        val totalModelSize = models.sumOf { it.sizeAfter }
        val estimatedMemoryUsage = totalModelSize * 1.5 // Model + runtime overhead
        
        return when {
            estimatedMemoryUsage > capabilities.memoryMB -> {
                // Models too large, use hybrid processing
                EdgeProcessingStrategy.HYBRID_CLOUD_EDGE
            }
            capabilities.bandwidthMbps < 10 -> {
                // Low bandwidth, prefer edge processing
                EdgeProcessingStrategy.EDGE_FIRST
            }
            capabilities.computePowerScore < 0.3 -> {
                // Limited compute, use cloud assistance
                EdgeProcessingStrategy.CLOUD_ASSISTED
            }
            else -> {
                // Capable edge device, process locally
                EdgeProcessingStrategy.FULL_EDGE
            }
        }
    }
    
    suspend fun deployToEdge(
        jobId: String,
        edgeNodeId: String,
        config: EdgeConfig
    ): EdgeDeploymentResult {
        
        return edgeDeploymentService.deploy(
            EdgeDeploymentRequest(
                jobId = jobId,
                edgeNodeId = edgeNodeId,
                config = config,
                healthCheckInterval = Duration.ofSeconds(30)
            )
        )
    }
}

// Bandwidth-aware streaming
@Component
class AdaptiveStreamingService(
    private val bandwidthMonitor: BandwidthMonitor,
    private val qualitySelector: QualitySelector,
    private val frameSkipper: FrameSkipper
) {
    
    suspend fun adaptStreamQuality(
        streamId: String,
        targetProcessingRate: Double
    ) {
        val currentBandwidth = bandwidthMonitor.getCurrentBandwidth(streamId)
        val stream = streamManager.getStream(streamId)
        
        // Calculate optimal quality based on bandwidth
        val optimalQuality = qualitySelector.selectQuality(
            availableBandwidth = currentBandwidth,
            targetFps = targetProcessingRate,
            currentQuality = stream?.qualityProfiles?.firstOrNull()
        )
        
        if (optimalQuality != stream?.qualityProfiles?.firstOrNull()) {
            // Switch quality
            streamManager.updateStreamQuality(streamId, optimalQuality)
            logger.info("Adapted stream $streamId quality to ${optimalQuality.name}")
        }
        
        // Adjust frame sampling if needed
        val optimalSamplingRate = calculateOptimalSamplingRate(
            bandwidth = currentBandwidth,
            quality = optimalQuality,
            targetProcessingRate = targetProcessingRate
        )
        
        frameSkipper.updateSamplingRate(streamId, optimalSamplingRate)
    }
    
    private fun calculateOptimalSamplingRate(
        bandwidth: Double,
        quality: QualityProfile,
        targetProcessingRate: Double
    ): Double {
        
        val frameSizeBits = quality.width * quality.height * 24 // Assuming 24-bit color
        val maxFramesPerSecond = (bandwidth * 1_000_000) / frameSizeBits // Convert Mbps to bps
        
        return minOf(maxFramesPerSecond, targetProcessingRate, quality.fps.toDouble())
    }
}

Security Implementation

Video Data Protection 

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// Video data encryption and secure processing
@Component
class VideoSecurityService(
    private val encryptionService: EncryptionService,
    private val accessControlService: AccessControlService,
    private val auditService: AuditService
) {
    
    suspend fun secureVideoStream(
        streamId: String,
        encryptionLevel: EncryptionLevel
    ): SecureStreamConfig {
        
        val stream = streamManager.getStream(streamId)
            ?: throw VideoStreamNotFoundException(streamId)
        
        // Generate stream-specific encryption keys
        val encryptionKeys = when (encryptionLevel) {
            EncryptionLevel.BASIC -> generateAESKeys()
            EncryptionLevel.ADVANCED -> generateECCKeys()
            EncryptionLevel.MILITARY_GRADE -> generateQuantumResistantKeys()
        }
        
        // Configure secure RTMP/WebRTC
        val secureConfig = SecureStreamConfig(
            streamId = streamId,
            encryptionKeys = encryptionKeys,
            encryptionLevel = encryptionLevel,
            certificatePath = generateTLSCertificate(streamId),
            accessTokens = generateAccessTokens(streamId)
        )
        
        // Update stream with security config
        streamManager.updateSecurityConfig(streamId, secureConfig)
        
        // Audit log
        auditService.logStreamSecurityUpdate(streamId, encryptionLevel)
        
        return secureConfig
    }
    
    suspend fun sanitizeVideoFrame(frame: VideoFrame): VideoFrame {
        // Remove metadata that might contain sensitive information
        val sanitizedFrame = frame.copy(
            metadata = frame.metadata.filterKeys { key ->
                !key.contains("location", ignoreCase = true) &&
                !key.contains("gps", ignoreCase = true) &&
                !key.contains("device", ignoreCase = true)
            }
        )
        
        return sanitizedFrame
    }
    
    suspend fun anonymizeDetections(detections: List<Detection>): List<Detection> {
        return detections.map { detection ->
            when (detection.className.lowercase()) {
                "person", "face" -> {
                    // Blur/anonymize person detections if required
                    detection.copy(
                        attributes = detection.attributes.filterKeys { key ->
                            !key.contains("identity", ignoreCase = true) &&
                            !key.contains("name", ignoreCase = true)
                        }
                    )
                }
                "license_plate", "text" -> {
                    // Redact text-based detections
                    detection.copy(
                        attributes = mapOf(
                            "text" to "[REDACTED]",
                            "confidence" to detection.confidence
                        )
                    )
                }
                else -> detection
            }
        }
    }
    
    suspend fun validateVideoAccess(
        userId: String,
        streamId: String,
        operation: VideoOperation
    ): Boolean {
        
        val hasAccess = accessControlService.checkVideoAccess(
            userId = userId,
            streamId = streamId,
            operation = operation
        )
        
        // Audit access attempt
        auditService.logVideoAccess(
            userId = userId,
            streamId = streamId,
            operation = operation,
            granted = hasAccess
        )
        
        return hasAccess
    }
}

// Privacy compliance service
@Component
class VideoPrivacyComplianceService(
    private val faceBlurService: FaceBlurService,
    private val licensePlateRedactor: LicensePlateRedactor,
    private val dataRetentionService: DataRetentionService
) {
    
    suspend fun applyPrivacyFilters(
        jobId: String,
        privacyRules: List<PrivacyRule>
    ): PrivacyProcessingResult {
        
        val results = mutableListOf<PrivacyAction>()
        
        privacyRules.forEach { rule ->
            when (rule.type) {
                PrivacyRuleType.FACE_ANONYMIZATION -> {
                    val blurredFrames = applyFaceBlurring(jobId, rule)
                    results.add(PrivacyAction(rule.type, blurredFrames))
                }
                PrivacyRuleType.LICENSE_PLATE_REDACTION -> {
                    val redactedFrames = applyLicensePlateRedaction(jobId, rule)
                    results.add(PrivacyAction(rule.type, redactedFrames))
                }
                PrivacyRuleType.PERSON_TRACKING_RESTRICTION -> {
                    disablePersonTracking(jobId)
                    results.add(PrivacyAction(rule.type, 0))
                }
                PrivacyRuleType.DATA_RETENTION_LIMIT -> {
                    scheduleDataDeletion(jobId, rule.retentionPeriod!!)
                    results.add(PrivacyAction(rule.type, 1))
                }
            }
        }
        
        return PrivacyProcessingResult(
            jobId = jobId,
            appliedRules = privacyRules,
            actions = results,
            processingTime = measureTime { /* Processing time calculation */ }
        )
    }
    
    private suspend fun applyFaceBlurring(jobId: String, rule: PrivacyRule): Int {
        val job = analysisJobRepository.findById(jobId)!!
        val faceDetections = resultsAggregator.getDetectionsByClass(jobId, "face")
        
        var blurredFrames = 0
        
        faceDetections.forEach { detection ->
            val blurSuccess = faceBlurService.blurFace(
                streamId = job.streamId!!.toString(),
                frameNumber = detection.frameNumber,
                boundingBox = detection.detections.first().boundingBox,
                blurIntensity = rule.intensity ?: 0.8
            )
            
            if (blurSuccess) {
                blurredFrames++
            }
        }
        
        return blurredFrames
    }
    
    suspend fun generatePrivacyReport(jobId: String): PrivacyComplianceReport {
        val job = analysisJobRepository.findById(jobId)!!
        val detections = resultsAggregator.getResults(jobId)
        
        // Analyze privacy-sensitive detections
        val faceCount = detections.count { it.detections.any { d -> d.className == "face" } }
        val personCount = detections.count { it.detections.any { d -> d.className == "person" } }
        val licensePlateCount = detections.count { it.detections.any { d -> d.className == "license_plate" } }
        
        return PrivacyComplianceReport(
            jobId = jobId,
            sensitiveDataFound = faceCount > 0 || personCount > 0 || licensePlateCount > 0,
            faceDetections = faceCount,
            personDetections = personCount,
            licensePlateDetections = licensePlateCount,
            dataRetentionPlan = dataRetentionService.getRetentionPlan(jobId),
            complianceStatus = determineComplianceStatus(faceCount, personCount, licensePlateCount),
            recommendations = generatePrivacyRecommendations(faceCount, personCount, licensePlateCount)
        )
    }
}

Next Steps:

  • Implement advanced object tracking algorithms (SORT, DeepSORT, ByteTrack)
  • Set up distributed model serving with TensorFlow Serving and TorchServe
  • Add support for custom vision model deployment and A/B testing
  • Implement video content understanding with scene analysis and activity recognition
  • Create comprehensive video analytics dashboard with real-time monitoring capabilities
Content Publishing Service - Technical Implementation