Paper Title
Eye Tracking and Head Pose Estimation for Driver Monitoring on Raspberry PI

mostly the automobile accidents are caused by distracted driving. Monitoring driver’s eyes can help in detecting state of mind and alertness of driver and thus can reduce risk of accidents. Proposed system includes three main parts 1) Facial feature tracking 2) Eye gaze and 3D head pose estimation 3) Eyes off the road and fatigue detection. Video feed from camera installed on car dashboard tracks features of driver in real time (25FPS). Infrared illuminator is used at night time to detect facial features clearly without distracting driver. Image processing algorithm is developed in OpenCV to estimate where the driver is looking by combining 3D head pose estimation and eye gaze estimation. Algorithm is implemented on Raspberry Pi board to make a compact embedded system. Different zones are defined as points few of which are eyes off the road points. SVM is used to train and classify different combinations of gaze and head pose angles to determine exact point of gaze. Based on algorithm output if driver’s eyes are off the road or eyes are closed due to fatigue then accordingly audio and steering vibration warnings are given to driver. Index Terms - Driver monitoring system, Eye gaze tracking, Head pose estimation, Raspberry Pi.