Loss Prevention: How It Works#

Table of Contents#

  1. System Overview

  2. Architecture Diagrams

  3. Configuration Management

  4. System Benefits

System Overview#

The Loss Prevention Pipeline System is a comprehensive video analytics platform designed for retail loss prevention using Intel hardware acceleration. The system processes multiple camera feeds simultaneously, applying AI-powered detection and classification models to identify potential security incidents.

Key Features#

  • Multi-camera Support: Simultaneous processing of multiple camera feeds

  • AI-Powered Analytics: Object detection and classification using YOLO and EfficientNet models

  • Intel Hardware Optimization: Leverages CPU, GPU, and NPU acceleration

  • Real-time Processing: GStreamer-based pipeline for low-latency video processing

  • Flexible Configuration: JSON-based configuration for cameras, workloads, and pipelines

  • Performance Monitoring: Comprehensive metrics collection and analysis

  • Containerized Deployment: Docker-based deployment with Intel DL Streamer

Architecture Diagrams#

Component Architecture#

        graph TB
    subgraph "Configuration Layer"
        CWC[camera_to_workload.json]
        WPC[workload_to_pipeline.json]
    end

    subgraph "Pipeline Generation Layer"
        PGL[Pipeline Generator]
        CL[Config Loader]
    end

    subgraph "Execution Layer"
        GST[GStreamer Runtime]
        ENV[Environment Manager]
        DEV[Device Manager]
    end

    subgraph "Processing Components"
        subgraph "Source"
            FS[filesrc]
            DEC[decodebin]
        end

        subgraph "AI Processing"
            ATT[gvaattachroi]
            DET[gvadetect]
            TRK[gvatrack]
            CLS[gvaclassify]
        end

        subgraph "Output"
            CNV[gvametaconvert]
            PUB[gvametapublish]
            TEE[tee]
            SINK[fakesink/fpsdisplaysink]
        end
    end

    subgraph "Performance Tools"
        BM[Benchmark Scripts]
        MC[Metrics Collection]
        AN[Analysis Tools]
    end

    CWC --> CL
    WPC --> CL
    CL --> PGL

    PGL --> GST
    GST --> ENV
    ENV --> DEV

    GST --> FS
    FS --> DEC
    DEC --> ATT
    ATT --> DET
    DET --> TRK
    TRK --> CLS
    CLS --> CNV
    CNV --> PUB
    CNV --> TEE
    TEE --> SINK

    GST --> BM
    BM --> MC
    MC --> AN

Configuration Flow#

        flowchart LR
    subgraph "Configuration Files"
        CAM[camera_to_workload.json \n• Camera definitions \n• Workload assignments \n• ROI specifications]
        WORK[workload_to_pipeline.json \n• Pipeline steps \n• Model configurations \n• Device assignments]
    end

    subgraph "Processing"
        LOAD[Config Loader]
        NORM[Normalization]
        MERGE[Pipeline Consolidation]
    end

    subgraph "Output"
        GST[GStreamer Command]
        EXEC[Pipeline Execution]
    end

    CAM --> LOAD
    WORK --> LOAD
    LOAD --> NORM
    NORM --> MERGE
    MERGE --> GST
    GST --> EXEC

Component Overview#

Core Components#

1. Pipeline Generator (gst-pipeline-generator.py)#

Purpose: Dynamic GStreamer pipeline generation based on configuration Key Functions:

  • Configuration loading and validation

  • Pipeline signature generation for optimization

  • Dynamic element creation based on workload requirements

  • Device-specific environment variable management

2. Configuration Management#

Files:

  • camera_to_workload.json: Camera definitions and workload assignments

  • workload_to_pipeline.json: Pipeline step definitions

3. GStreamer Elements#

Source Elements:

  • filesrc: Video file input

  • decodebin: Automatic video decoding

AI Processing Elements:

  • gvaattachroi: Region of Interest attachment

  • gvadetect: Object detection using YOLO models

  • gvatrack: Object tracking across frames

  • gvaclassify: Object classification using EfficientNet

Output Elements:

  • gvametaconvert: Metadata format conversion

  • gvametapublish: Results publishing to files

  • tee: Pipeline branching for multiple outputs

Environment Configuration#

Device-specific environment files:

  • /res/all-cpu.env: CPU optimization settings

  • /res/all-gpu.env: GPU acceleration settings

  • /res/all-npu.env: NPU configuration

Configuration Management#

Camera Configuration#

{
  "lane_config": {
    "cameras": [
      {
        "camera_id": "cam1",
        "fileSrc": "video-file.mp4",
        "width": 1920,
        "fps": 15,
        "workloads": ["detection", "classification"],
        "region_of_interest": {
          "x": 0.1,
          "y": 0.1,
          "x2": 0.9,
          "y2": 0.9
        }
      }
    ]
  }
}

Workload Configuration#

{
  "workload_pipeline_map": {
    "detection": [
      {
        "type": "gvadetect",
        "model": "yolov5s",
        "device": "GPU",
        "precision": "FP16"
      }
    ],
    "classification": [
      {
        "type": "gvaclassify",
        "model": "efficientnet-b0",
        "device": "NPU",
        "precision": "INT8"
      }
    ]
  }
}

System Benefits#

Performance Advantages#

  • Hardware Optimization: Native Intel hardware acceleration

  • Pipeline Efficiency: Optimized GStreamer pipelines with minimal overhead

  • Parallel Processing: Multi-camera, multi-workload concurrent execution

  • Resource Management: Intelligent device allocation and load balancing

Scalability Features#

  • Configuration-Driven: Easy addition of new cameras and workloads

  • Modular Design: Independent processing pipelines

  • Container-Based: Horizontal scaling with Docker orchestration

  • Performance Monitoring: Real-time metrics for optimization

This architecture documentation provides a comprehensive overview of the Loss Prevention Pipeline System. For implementation details, refer to the source code and configuration files in the repository.