Attention recognition of drivers based on head pose estimation

Recognition of driverpsilas attention plays an important role in driver assistance system, which can recognize the driverpsilas state and prevent the driver from distracting his attention. To some extent, the driverpsilas head pose implies his attentionpsilas orientation. In this paper we describe a vision-based scheme of estimating the driverpsilas head pose, which is based on estimating the relative pose between adjacent views. Scale-Invariant Feature Transform (SIFT) descriptors are utilized to match the corresponding feature points between two adjacent views. We also bring forward a method to capture the key frames online through fusing the smoothness assumption of headpsilas motion and other prior motion information, in order to eliminate the accumulating errors during continuous pose accumulation. The appropriate key frames will be chosen as for the different frames within different pose range to refresh the result of pose estimation, which can reduce the accumulating error during the process of head pose estimation greatly. The experiment shows that our system can work very well, even within large rotation scope and over long video sequences.