An AI-powered system that tracks head motion and monitors eye closure and blink rates to detect distraction and drowsiness in real-time, enabling early intervention and preventing potential accidents.
Key Features
- Real-time head and eye tracking
- Detection of drowsiness and distraction
- Alerts and intervention mechanisms
- Integration with vehicle control systems
Tech Stack
Python, OpenCV, TensorFlow, Keras, Raspberry Pi
Distraction Detection - Enhancing Driver Safety
We developed an AI-powered system that monitors head motion, eye closure, and blink rates to detect signs of distraction and drowsiness in real-time. Using advanced computer vision and machine learning algorithms, the system continuously tracks driver behavior to identify early warning signs of fatigue or inattentiveness. This proactive detection enables early intervention, helping to prevent potential accidents and improve road safety. Designed for integration into vehicle systems, the solution is ideal for fleet management, logistics, and transportation industries where driver alertness is critical. By providing real-time alerts, the system ensures drivers can respond quickly to potential hazards, reducing the risk of accidents and enhancing overall safety on the road. This scalable solution is a key asset for companies focused on reducing operational risks and ensuring driver well-being.
Key Problem
Developing a system to accurately detect distraction and drowsiness in real-time, while minimizing false positives, was a critical challenge. Ensuring it performed well in varying light conditions and with different users required extensive testing.
Project Highlights
- ✔️ Real-time driver monitoring
- ✔️ Early warning alerts for distraction
- ✔️ Scalable for commercial vehicle fleets
- ✔️ Drowsiness detection with high accuracy