DEVELOPMENT OF VEHICLE DRIVER DROWSINESS DETECTION SYSTEM USING EYE ASPECT RATIO

Abstract

A countless number of people drive on the highway day and night. Taxi drivers, bus drivers, truck drivers, and people traveling long-distance suffer from lack of sleep. Due to which it becomes very dangerous to drive when feeling sleepy. The majority of accidents happen due to the drowsiness of the driver. The major aim of this research is to develop a system that alerts the driver if any type of drowsiness occurs. The proposed approach to detect driver’s drowsiness is based on two levels: The face is detected from a video stream using facial landmark detection and the eye region is extracted. These facial landmarks are then used to compute the Eye Aspect Ratio (EAR) and are returned back to the driver. Numerous variety of images were considered for testing purposes. The obtained accuracy was found to be adequate. The proposed system will alert the driver when drowsiness is detected. Drowsiness detection is a safety technology that can prevent accidents that are caused by drivers who fell asleep while driving. To validate the proposed approach tests were conducted by using a variety of different images, such as the driver's face covered with a mask or glasses. The proposed approach uses EAR with adaptive thresholding to detect driver drowsiness in real-time. This is useful in situations when the drivers are used to the strenuous workload and drive continuously for long distances.