DRIVER ALERTNESS MONITORING USING MACHINE LEARNING AND VISION
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 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 manner. A sensitivity of 95.58% and specificity of 100% has been achieved in Support
Vector Machine based classification
