Real-Time Tracking of Head Motion
XU Yi hua, ZHU Yu wen, JIA Yun de (Department of Computer Science and Engineering, Beijing Institute of Technology, Beijing 100081)
This paper presents an improved real time head tracking method, which can realize robust tracking of a person's translation and turning around with a black/white camera. This method consists of two main steps: block feature based head tracking, and head geometry based correction. The block feature based tracking only uses low level image information and does not rely on the models of different objects, thus it can be used to track the free motion of the head. The correction step is introduced to address the shift caused by errors accumulating of block tracking. This step measures the displacements of the head contour in the tracking window of current image, and hereby to correct the results of block tracking. Moreover, to improve the performance of block tracking during person's turning around, we introduce a cylindrical model to approximate head, and extract and track features in the warped cylindrical surface. The resulting system provides robust and precise tracking over long sequence on a 350 megahertz microcomputer, and operates at 30 frames per second with the tracking window size of 120×180 pixels and the tracking sets of 80 features.