Eye Gaze Detection Using Hough Circle Transform

摘要

Driver distraction is one of the main causes of road accidents in Malaysia. There are three types of driver distraction discussed in this paper including visual distraction, manual distraction and cognitive distraction. Each of them is all described different context of distractions. Statistics shows that about 3 death per 10 000 registered vehicles reported by Malaysian Institute of Road Safety Research (MIROS) in 2016. Mercedes Benz, Volvo, Mazda and Lexus have introduced their driver assistant system for their certain models of their cars to monitor the condition of the drivers. 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. Demand for ADAS is caused by desire to build safer vehicles and roads in order to reduce the number of road accidents and by legislation. 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. So, this paper describes the eye distraction detection using computer vision approach. Our approach aims to determine whether the driver is distracted or not. We proposed two different modules for feature extraction focusing on eye gaze detection and percentage of eye closure (PERCLOS). Both output then will be integrated and combined using SVM classifier.