|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
TRACKING USING A HIERARCHICAL BAYESIAN FILTER
B. Stenger, A. Thayananthan, P. H. S. Torr, and R. Cipolla
This paper sets out a tracking framework, which is applied to the recovery of three-dimensional hand motion from an image sequence. The method handles the issues of initialization, tracking, and recovery in a unified way. In a single input image with no prior information of the hand pose, the algorithm is equivalent to a hierarchical detection scheme, where unlikely pose candidates are rapidly discarded. In image sequences a dynamic model is used to guide the search and approximate the optimal =02filtering equations. A dynamic model is given by transition probabilities between regions in parameter space and is learned from training data obtained by capturing articulated motion. The algorithm is evaluated on a number of image sequences, which include hand motion with self-occlusion in front of a cluttered background.
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