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.

Research on Railway Dispatcher Fatigue Detection Method Based on Deep Learning with Multi-Feature Fusion

“…The literature [13] compared human eye detection technologies based on neural network methods, support vector machine methods, cascade algorithms, etc., according to images and the PERCLOS (percentage of eyelid closure over the pupil over time, eye closure time per unit time) principle, and the authors designed a deep learning method to detect driver fatigue. The authors of [14], however, used a driver's facial image, which was collected with a camera, and employed the YOLO-LITE deep learning network and the Haar-like feature cascade for detection. In addition, they proposed a multi-layer perceptron (MLP), instead of the PerStat method of the PERCLOS method.…”

Driver’s Drowsiness Detection System

“…In this driver drowsiness issue, there are too many methods according to the parameters used to measure the sleepiness of the drivers, such as some are based on the respiratory signal of the driver. The problem that inspired us to start this work is that the methods are more focused/tiredness on the detection of than [1] its evolution or handled it we suggest a continuity of our previous framework in this interface for the detection of fatigue\tiredness as well as its evolution over time and its handling with a conversational assistant to detects the state of the driver via a camera by remaining discreet enough, but who in dangerous situations marks a vigilant and moral presence to try help to take the right decisions. Applications for face recognition use algorithms and ML to locate people's faces in bigger photos, which frequently include non-facial items like buildings, landscapes, and other human body parts like the feet or hands.…”