Driver drowsiness detection and tracking based on yolo with haar cascades and ERNN

Abstract

When it comes to dangerous drowsiness, the security of the driver and peoples surrounding him depends only on his decisions. This paper expose both of driver drowsiness detector and driving behaviour corrector method based on a conversational assistant agent able to discern and try to avoid driver sleepiness on the wheel, by using a camera to get face’s images of the driver in real time, and an agent displayed in the screen and monitors the driver's face in order to warn of drowsiness and to avoid a possible accident. For that, we used Haar cascade with a simplified Yolo-Lite merged with a tree word for detection, followed by the proposed PerStat method with MLP instead of PerClos method which gave a difference of (20%). For the recognition, and helped to raise the problem of the vanishing gradient being used as sequential pre-processing for an ERNN which will generate the agent feedbacks.