Computer Vision based drowsiness detection for motorized vehicles with Web Push Notifications

Since the advent of high-speed motorized vehicles drowsy driving accidents have claimed the lives of millions of people across the globe. With more research happening in advanced high-speed engines, the risk of such accidents has increased ten-fold times. To avoid such accidents, this paper proposes a Computer Vision-basedsystem drowsiness system for motorized vehicles with Web Push Notifications to notify the driver before any accident occurs. A real-time video system captures the face of the driver and a pre-trained machine learning model detects the eye boundaries from that real-time video stream. Then each eye is represented by 6 - coordinates (x,y) starting from the left corner of the eye and then working clockwise around the eye. The EAR (Ear Aspect Ratio) is calculated for 20 consecutive frames, which if less than a threshold sounds an alarm and sends an alert on your mobile device through a Web Push Notification. The alert when opened also shows some coffee shops near the driver's location to increase the driver's alertness.