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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 filtering 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.
Probabilistic algorithms, video analysis, tracking.
Bj? Stenger, Arasanathan Thayananthan, Roberto Cipolla, Philip H.S. Torr, "Model-Based Hand Tracking Using a Hierarchical Bayesian Filter", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 1372-1384, September 2006, doi:10.1109/TPAMI.2006.189
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