In-attention State Monitoring for a Driver Based on Head Pose and Eye Blinking Detection Using One Class Support Vector Machine

This paper proposes a model to detect inattention cognitive state of a driver during various driving situations. The proposed system predicts driver’s inattention state based on the analysis of eye blinking patterns and head pose direction. The study uses an infrared camera and several feature extraction stages such as modified census transform (MCT) to reduce the effect of light source change in real traffic environment. Also, we propose a new eye blinking detection using the difference between center and surround of Hough circle transform image. The local linear embedding (LLE) is used to extract real-time features of head movement. Finally, the driver’s cognitive states can be estimated by the one-class support vector machines (OCSVMs) using both eyes blinking patterns and head pose direction information. We implement a prototype of the proposed driver state monitoring (DSM) system. Experimental results show that the proposed system using OCSVM works well in real environment compared to the system that employs SVM.