Vision based Eye Closeness Classification for Driver’s Distraction and Drowsiness Using PERCLOS and Support Vector Machines: Comparative Study between RGB and Grayscale Images

Abstract

Driver inattention and drowsiness are part causes of road accidents in Malaysia. Based on statistics from the Royal Malaysian Police (2016), deaths from road accidents amount is 7, 512 in 2016 compared to 6, 706 in 2015, which is the highest number of deaths recorded. Hence, an assistant system is needed to monitor driver’s condition like some car manufacturers introduced to their certain models of car. The assistant system is a part of main system known as advanced driver assistance systems (ADAS) are systems developed to enhance vehicle systems for safety and better driving. The system is expected to gather accurate input, be fast in processing data, accurately predict context, and react in real time. Suitable approach is needed to fulfil the system expectation. This paper describes the drowsiness and driver in attention detection and classification using computer vision approach. Our approach aims to classify driver drowsiness and inattention using computer vision. We proposed a technique to classify drowsiness into three different classes of eye state; open, semi close and close. The classification is done by using feature extraction method, percentage of eye closure (PERCLOS) technique and Support Vector Machine (SVM) classifier. We examined and analysed the Grayscale and RGB images using mentioned techniques.