Proactive Fatigue Detection for Improved Road Safety and Driver Awareness

Fatigue detection is essential for ensuring safety and efficiency in various fields, particularly in transportation and workplace environments. This project focuses on developing a real-time fatigue detection system using computer vision and machine learning techniques. By analyzing head movements and eye activity, the system identifies early signs of drowsiness and provides timely alerts. The project utilizes OpenCV and Media Pipe for real-time facial feature tracking and analysis. These technologies enable precise detection of eye behavior and head posture, ensuring reliable fatigue assessment. Machine learning techniques further enhance the accuracy of the system, making it adaptable to different environments and lighting conditions. This study includes case studies and experimental evaluations to assess the effectiveness of the proposed approach. By examining real-world applications, the project aims to identify best practices and optimization strategies for future development. Ultimately, this research contributes to the advancement of automated fatigue monitoring systems, helping to improve safety and productivity in high-risk settings.