Loss Prevention Pipeline System Architecture

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 Architecture

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.