DriveAlert: Drowsiness Detection

The DriveAlert is a driver drowsiness detection system designed to enhance road safety by monitoring real-time facial cues and identifying signs of fatigue. It provides timely alerts to help prevent accidents caused by drowsy driving.

Note: This project is developed for academic purposes and is not deployed or hosted online.

Project Overview

This system was developed to detect driver fatigue using real-time monitoring of facial behavior. By analyzing eye movement and alertness indicators, the system helps identify early signs of drowsiness and triggers alerts to ensure safer driving conditions.

My Role

Developed the drowsiness detection logic and system flow.
Implemented real-time monitoring functionality.
Worked on alert system for fatigue detection.
Assisted in system testing and optimization.
Debugged and improved detection accuracy.

Technologies Used

Core Technology

Python
OpenCV
Machine Learning (basic classification)
Image Processing Techniques

Tools

VS Code
Python Libraries
OpenCV

Key Features

Real-time drowsiness detection using facial monitoring
Eye movement and alertness analysis
Automated warning alerts for drivers
Improved road safety mechanism
Lightweight and real-time processing system

Challenges & Solutions

One of the main challenges was ensuring accurate detection under different lighting conditions and real-world scenarios. This was addressed by optimizing image processing techniques and refining detection thresholds for better reliability.

Project Outcome

The system successfully demonstrates how computer vision can be applied to improve road safety by detecting early signs of driver fatigue and providing timely alerts.

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