AutomoBee Vehicle Detection System

Real-time traffic monitoring system using computer vision and AI, with WebSocket-based communication for efficient real-time updates.

Architecture

Backend

• Core Controller (AutomoBee)

  • Centralized system initialization and coordination
  • Resource management and cleanup
  • Component lifecycle management

• Services

  • WebSocket server for real-time communication
  • Async stream manager for video processing
  • Zone-based detection service
  • Performance monitoring and optimization

Frontend

• Components
  • Real-time dashboard with WebSocket updates
  • Interactive map with zone visualization
  • HLS video stream viewer
  • Performance metrics display

Features

• Asynchronous video stream processing
• Batch-based vehicle detection
• Zone-based monitoring
• Real-time WebSocket updates
• Automatic performance optimization
• Resource usage monitoring
• Comprehensive error handling

Setup

1. Create Python virtual environment:

python -m venv .venv
source .venv/bin/activate  # Linux/Mac
# or
.venv\Scripts\activate  # Windows

2. Install dependencies:

pip install -r requirements.txt

3. Configure environment:

cp .env.example .env
# Edit .env with your settings

4. Initialize configuration:

mkdir -p config logs
cp config/cameras.json.example config/cameras.json
# Edit cameras.json with your camera configurations

5. Start the system:

python main.py

Configuration

Camera Configuration (config/cameras.json)

{
  "camera_id": {
    "name": "Camera Name",
    "lat": 36.1699,
    "lng": -115.1398,
    "stream_url": "rtsp://camera-url",
    "fps": 30,
    "zones": [
      {
        "name": "Zone 1",
        "coordinates": [
          {"lat": 36.1699, "lng": -115.1398},
          {"lat": 36.1700, "lng": -115.1399},
          {"lat": 36.1701, "lng": -115.1397}
        ]
      }
    ]
  }
}

Environment Variables

See .env.example for all available configuration options.

Development

Project Structure

automobee/
├── src/
│   ├── backend/
│   │   ├── core/
│   │   │   └── automobee.py
│   │   └── services/
│   │       ├── detection_service.py
│   │       ├── stream_manager.py
│   │       ├── websocket_server.py
│   │       └── metrics_collector.py
│   └── frontend/
│       ├── components/
│       │   ├── map.js
│       │   └── stream-viewer.js
│       └── services/
│           └── websocket.js
├── config/
│   ├── cameras.json
│   └── logging.yaml
├── models/
│   ├── yolov8m.pt
│   └── vehicle_classifier.pt
└── logs/
    ├── automobee.log
    ├── error.log
    └── metrics.log

Testing

Run tests with:

pytest tests/

Monitoring

Access metrics and health information:

• System metrics: logs/metrics.log
• Application logs: logs/automobee.log
• Error tracking: logs/error.log

Performance Optimization

The system automatically optimizes:

• Batch processing size
• Frame skip rate
• Detection confidence threshold
• Resource utilization

Monitor performance through:

• Detection rate per camera
• Classification confidence
• Processing latency
• Memory usage
• GPU utilization

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

MIT License - see LICENSE file for details