DRIVER DROWSINESS DETECTION USING MACHINE LEARNING

Abstract

Drowsiness and fatigue are one of the main causes leading to road accidents. They can be prevented by taking an effort to get enough sleep before driving, drinking coffee or energy drink, or having rest when the signs of drowsiness occur. The popular drowsiness detection method uses complex methods, such as EEG and ECG. This method has high accuracy for its measurement but it needs to use contact measurement and it has many limitations on driver fatigue and drowsiness monitor[18]. Thus, it is not comfortable to be used in real-time driving. This paper proposes a way to detect drowsiness signs among drivers by measuring the eye closing rate and yawning. This project describes how to detect the eyes and mouth in a video recorded from the experiment conducted by MIROS (Malaysian Institute of Road Safety). In the video, a participant will drive the driving simulation system and a webcam will be placed in front of the driving simulator. The video will be recorded using the webcam to see the transition from awake to fatigue and finally, drowsy. The designed system deals with detecting the face area of the image captured from the video. The purpose of using the face area so it can narrow down to detect eyes and mouth within the face area. Once the face is found, the eyes and mouth are foundby creating the eye for left and right eye detection and also mouth detection. The parameters of the eyes and mouth detection are created within the face image. The video were change into images frames per second. From there, locating the eyes and the mouth can be performed. Once the eyes are located, measuring the intensity changes in the eye area determines whether the eyes are open or closed. If the eyes are found closed for 4 consecutive frames, it is confirmed that the driver is indrowsiness condition.