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Model-based hand tracking using a hierarchical Bayesian filter
September 2006 (vol. 28 no. 9)
pp. 1372-1384
B. Stenger, Toshiba Corp. R&D Center, Kawasaki
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

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Index Terms:
Bayesian methods,Particle tracking,Image sequences,Particle filters,State-space methods,Filtering,Video sequences,Parameter estimation,Robustness,Target tracking,tracking.,Probabilistic algorithms,video analysis
Citation:
B. Stenger, A. Thayananthan, P.H.S. Torr, R. Cipolla, "Model-based hand tracking using a hierarchical Bayesian filter," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 9, pp. 1372-1384, Sept. 2006, doi:10.1109/TPAMI.2006.189
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