A hierarchical framework for face tracking using state vector fusion for compressed video

Abstract

Faces usually are the most interesting objects in certain categories of video like home videos and news clips. In this paper a novel sensor fusion based face tracking system is presented that tracks faces in compressed video, and aids automatic video indexing. Tracking is done by fusing the measurements from three independent sensors - motion and colour based trackers (derived from [2]) and a face detector (presented in [1]) using a novel hierarchical framework based on Kaiman filter state vector fusion. The tracking results show that the fused results are better than those of any individual sensors or their mean.