Vigilance Alertness System

Driver fatigue and drowsiness are critical factors leading to road accidents, accounting for a significant percentage of such incidents globally. The proposed "Vigilance Alertness System" utilizes machine learning and computer vision techniques to identify driver drowsiness in instantly. By examining facial landmarks, specifically eye motions and blinks, the system assesses the driver's alertness. Machine learning enables accurate pattern recognition in facial features, while computer vision processes video frames captured via a webcam. When signs of fatigue are detected, an audio alert is triggered, prompting the driver to take timely action. This system offers a practical and efficient solution for enhancing road safety, particularly for long-distance drivers. The implemented model achieves a high detection accuracy of 95.5%, demonstrating its reliability in real-world scenarios.