DRIVER DROWSINESS MONITORING SYSTEM USING VISUAL BEHAVIOUR AND MACHINE LEARNING

Abstract

Drowsy driving is one of the major causes of road accidents and death. Hence, 
detection of driver’s fatigue and its indication is an active research area. Most of the 
conventional methods are either vehicle based, or behavioral based or physiological 
based. Few methods are intrusive and distract the driver, some require expensive 
sensors and data handling. Therefore, in this study, a low cost, real time driver’s 
drowsiness detection system is developed with acceptable accuracy. In the developed 
system, a webcam records the video and driver’s face is detected in each frame 
employing image processing techniques. Facial landmarks on the detected face are 
pointed and subsequently the eye aspect ratio, mouth opening ratio and nose length 
ratio are computed and depending on their values, drowsiness is detected based on 
developed adaptive thresholding. Machine learning algorithms have been
implemented as well in an offline manne