A real time deep learning based driver monitoring system - Mohd Hanafi, Mohamad Faris Fitri; Md. Nasir, Mohammad Sukri Faiz; Wani, Sharyar; Abdulghafor, Rawad Abdulkhaleq Abdulmolla; Gulzar, Yonis; Hamid, Yasir (IIUM, 2021)
Road traffic accidents almost kill 1.35 million people around the world. Most of these accidents take place in low and middle-income countries and costs them around 3% of their gross domestic product. Around 20% of the traffic accidents are attributed to distracted drowsy drivers. Many detection systems have been designed to alert the drivers to reduce the huge number of accidents. However, most of them are based on specialized hardware integrated with the vehicle. As such the installation becomes expensive and unaffordable especially in the low- and middle-income sector. In the last decade, smartphones have become essential and affordable. Some researchers have focused on developing mobile engines based on machine learning algorithms for detecting driver drowsiness. However, most of them either suffer from platform dependence or intermittent detection issues. This research aims at developing a real time distracted driver monitoring engine while being operating system agnostic using deep learning. It employs a CNN for detection, feature extraction, image classification and alert generation. The system training will use both openly available and privately gathered data.