Vision based eye closeness classification for driver's distraction and drowsiness using PERCLOS and support vector machines
Driver inattention and drowsiness are part causes of road accidents in Malaysia. Statistics shows that about 3 deaths per 10 000 registered vehicles reported by Malaysian Institute of Road Safety Research (MIROS) in 2016. 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. Desire to build safer vehicles and roads to reduce the number of fatalities and by legislation cause demand for ADAS. However, there are several challenges to design, implement, deploy, and operate ADAS. 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. There are four types of detection including by using physiological sensors, driver performance, computer vision, and hybrid system. 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. Two types of data training and testing images of drivers' eye condition which are eye with spectacles and eye without spectacles have been used. The results show that the proposed technique can classify the classes of distraction or drowsiness with high accuracy. Furthermore, by using only one type of eye condition data training, we are also able to classify the three different drowsiness classes regardless the eye conditions.