Driver Drowsiness Detection

Project Overview

The Driver Drowsiness Detection project is a computer vision-based system that detects driver drowsiness in real-time. The system uses a camera to monitor the driver's facial features and eye movements. includes vision models from the Driver Drowsiness Detection - GitHub, which is a collection of pre-trained models for eye(s) state detection, eye aspect ratio calculation, and drowsiness detection by closed eye state. Also releases from Driver Drowsiness Detection - Release Beta which includes the latest release of the project.

Key Features

• Data-driven decision-making using supervised and unsupervised algorithms.
• Real-time trend analysis and prediction models.
• Customizable agent configurations for diverse farming senarios and production gaols.
• Comprehensive visualization tools for model performance insights.

Technologies Used

• Programming: Python
• Machine Learning Frameworks: TensorFlow, PyTorch
• Data Processing Tools: Pandas, NumPy
• Visualization: Matplotlib, Plotly, Opencv
• Version Control: Git, GitHub

Future Enhancements

The project aims to incorporate dynamic knowledgebase to predict better driver drowsiness patterns based on historical data and current trends. Future iterations will also focus on improving the accuracy of drowsiness detection and reducing false positives. Additionally, enhancements include integrating the system with vehicle control mechanisms to alert or assist the driver in real-time.