DriverTrac/docs

Driver DSMS/ADAS Real-Time Validator

A Streamlit-based application for real-time Driver State Monitoring System (DSMS) and Advanced Driver Assistance System (ADAS) validation using computer vision and deep learning.

📋 Project Status

Current Status: ⚠️ Requires Critical Fixes Before Use

• Dependencies: 2/11 installed (18%)
• Code Quality: Multiple critical bugs identified
• Performance: Not optimized for low-spec CPUs
• Functionality: Non-functional (will crash on execution)

🚀 Quick Start

1. Check Current Status

python3 check_dependencies.py

2. Install Dependencies

pip install -r requirements.txt

Note: This will download ~2GB and require ~5GB disk space.

3. Configure

Edit track_drive.py and set your Roboflow API key:

'roboflow_api_key': 'YOUR_ACTUAL_KEY_HERE',

4. ⚠️ DO NOT RUN YET

The code has critical bugs that must be fixed first. See ASSESSMENT_REPORT.md for details.

📚 Documentation

• ASSESSMENT_REPORT.md - Comprehensive evaluation, issues, and improvement plan
• QUICK_START.md - Installation and setup guide
• requirements.txt - Python dependencies

🔍 What This Project Does

DSMS (Driver State Monitoring)

• Drowsiness detection (PERCLOS)
• Distraction detection (phone use, looking away)
• Smoking detection
• Seatbelt detection
• Driver absence detection

ADAS (Advanced Driver Assistance)

• Forward Collision Warning (FCW)
• Lane Departure Warning (LDW)
• Pedestrian detection
• Tailgating detection
• Hard braking/acceleration detection
• Overspeed detection

🛠️ Technology Stack

• Streamlit: Web UI framework
• YOLOv8n: Object detection (vehicles, pedestrians, phones)
• MediaPipe: Face mesh analysis for PERCLOS
• OpenCV: Image processing and optical flow
• Roboflow: Seatbelt detection API
• VideoMAE: Action recognition (⚠️ too heavy, needs replacement)
• scikit-learn: Anomaly detection

⚠️ Known Issues

Critical Bugs (Must Fix)

1. Optical Flow API Error: calcOpticalFlowPyrLK used incorrectly - will crash
2. VideoMAE JIT Scripting: Will fail - transformers can't be JIT scripted
3. YOLO ONNX Parsing: Incorrect output format assumption
4. ONNX Export: Runs on every load instead of conditionally

Performance Issues

1. VideoMAE Too Heavy: Not suitable for low-spec CPUs
2. All Models Load at Startup: Slow initialization
3. No Model Quantization: VideoMAE runs in FP32
4. Untrained Isolation Forest: Produces random predictions

See ASSESSMENT_REPORT.md for complete analysis.

📊 Performance Targets

Target Hardware: Low-spec CPU (4 cores, 2GHz, 8GB RAM)

Current (Estimated After Fixes):

• FPS: 5-8
• Memory: 4-6GB
• CPU: 70-90%

Target (After Optimizations):

• FPS: 12-15
• Memory: 2-3GB
• CPU: <80%
• Accuracy: >90% precision, >85% recall

🗺️ Implementation Roadmap

Phase 1: Critical Fixes (Week 1)

• Fix optical flow implementation
• Remove VideoMAE JIT scripting
• Fix YOLO ONNX parsing
• Add error handling
• Install and test dependencies

Phase 2: Performance Optimization (Week 2)

• Replace VideoMAE with lightweight alternative
• Implement lazy model loading
• Optimize frame processing pipeline
• Add smart frame skipping

Phase 3: Accuracy Improvements (Week 3)

• Train Isolation Forest
• Improve PERCLOS calculation
• Add temporal smoothing
• Fix distance estimation

Phase 4: Testing & Validation (Week 4)

• Unit tests
• Integration tests
• Performance benchmarking
• Documentation

🧪 Testing

After fixes are implemented:

# Run dependency check
python3 check_dependencies.py

# Run application
streamlit run track_drive.py

📝 Requirements

• Python 3.8+
• ~5GB disk space
• Webcam or video file
• Roboflow API key (free tier available)

🤝 Contributing

Before making changes:

1. Read ASSESSMENT_REPORT.md
2. Follow the implementation plan
3. Test on low-spec hardware
4. Document changes

📄 License

[Add your license here]

🙏 Acknowledgments

• Ultralytics for YOLOv8
• Google for MediaPipe
• Hugging Face for transformers
• Roboflow for model hosting

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Last Updated: November 2024 Status: Assessment Complete - Awaiting Implementation