Real-Time EAR Based Drowsiness Detection Model for Driver Assistant System
Parakram Singh, Shivanshu Mahim, Surya Prakash V · 2022
Driver exhaustion can be an essential factor in an excessive number of vehicle accidents. Detection technology or avoiding drowsiness at the wheel may be a significant challenge in collision avoidance systems. This research aims to develop a model of a drowsiness detection system that can track whether the driver's eyes are open or closed in real-time. It is thought that early identification of signs of driver exhaustion, such as watching the eyes, is often enough to avoid a road accident. The detection of fatigue necessitates a series of eye photographs to observe the eye movements and blink patterns. The proposed system is based on eye localization, which entails scanning the captured image to determine the eyes' location using the facial landmarks algorithm with the help of Dlib and OpenCV. It determines whether they are open or closed to detect drowsiness, adding a feature for the driver assistant system to avoid collisions.