Developing a Driver Fatigue Monitoring System Utilizing Visual Behaviour Using Machine Learning
Keywords:
drowsiness detection, visual behaviour, eye aspect ratio, mouth opening ratio, nose length ratio.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 behavioural 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 this system, a webcam records the video and the
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 and nose
length ratio are computed and depending on their values, drowsiness is detected based on
developed adaptive threshold. Machine learning algorithms have been implemented as well
in an offline manner.
