Driver Drowsiness Monitoring System Using Visual Behavior And Machine Learning

One of the leading causes of car accidents is drowsy driving. The driver drowsiness detection system detects fatigue and helps to avoid accidents. Classic methods include vehicle-based, behavior-based, and physiological-based techniques. Some of these solutions, however, are inconvenient for the driver, while others necessitate the purchase of costly sensors and devices. As a result, this project demonstrates how to build low-cost, real-time driver drowsiness with long-term accuracy. In this system, drowsiness is detected using a webcam. This webcam captures the driver's frames using image processing techniques. For each frame, the eye aspect ratio (EAR) and mouth opening ratio (MOR) is calculated. The computed values and the observed threshold values are used to detect drowsiness.