Project Overview
The Distracted Driver Detection system uses computer vision and deep learning to identify distracted driving behaviors in real-time. The system employs a VGG-16 convolutional neural network to classify various driver states and provide alerts for safety-critical situations.
Deep Learning
VGG-16
Computer Vision
Python
TensorFlow
Safety Systems
Key Features
- Real-time Detection: Instant identification of distracted driving behaviors
- Multi-class Classification: Recognition of various distraction types (phone use, eating, etc.)
- High Accuracy: VGG-16 architecture optimized for driver monitoring
- Alert System: Immediate notifications for dangerous behaviors
- Data Logging: Comprehensive recording of driver behavior patterns
- Privacy-Focused: Local processing to protect driver privacy
Technical Implementation
The system leverages advanced computer vision and deep learning techniques:
- VGG-16 Architecture: Pre-trained CNN fine-tuned for driver behavior classification
- Image Processing: Real-time video frame analysis and preprocessing
- Transfer Learning: Efficient training using pre-trained weights
- Multi-class Classification: Detection of 10 different driver states
- Real-time Processing: Optimized inference for low-latency alerts
- Data Augmentation: Enhanced training with synthetic data generation
Machine Learning Pipeline
The ML pipeline includes:
- Large-scale dataset collection of driver behavior images
- Data preprocessing and augmentation for robust training
- Transfer learning with VGG-16 pre-trained on ImageNet
- Fine-tuning for specific driver behavior classification
- Model optimization for real-time inference
- Continuous validation and performance monitoring
Safety Applications
The system addresses critical road safety challenges:
- Prevention of accidents caused by distracted driving
- Real-time driver behavior monitoring and feedback
- Integration with vehicle safety systems
- Data collection for safety research and policy development
- Support for autonomous vehicle safety protocols
Future Enhancements
Planned improvements include:
- Integration with vehicle infotainment systems
- Advanced alert mechanisms (audio, haptic feedback)
- Cloud-based analytics for fleet management
- Integration with insurance telematics
- Multi-camera support for comprehensive monitoring