Bayesian Learning of Driver Head Motion via Interframe Optical Flow Clustering

Abstract

In this paper, we present an approach to the driver head motion tracking problem using Bayesian learning and inter-frame optical flow clustering. Two cameras (one operating in visual band and the other in the infra-red range) are mounted on vehicle dashboard to determine the driver head motion by examining the temporal variation of video-histogram differences (VHD). Progresively increasing VHD indicates that the driver head moves from its steady position. An optical flow map is generated as a measure that would lead the tracker in the direction of head motion in synthetically generated volumetric view geometry involving a sequence of motion frames. Experiments show that the proposed system can track the driver head and identify the motion as left or right in two consecutive video frames parallel to the image plane of the camera or forward and backward direction from one frame to next. This simplifies the problem of tackling head movement with arbitrary motion in space via considering two consecutive frames at a time and marking the motion from the current frame to the next in sequence. Since the head position is marked in each frame with its optical flow track, the arbitrary motion is viewed as a classification problem of belonging to left-right, right-left, backward-forward, and forward-backward in the inter-frame duration via the Bayesian learning process of the Gaussian density.