Model-Based Hand Tracking Using a Hierarchical Bayesian Filter

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Model-Based Hand Tracking Using a Hierarchical Bayesian Filter
This paper sets out a tracking framework, which is applied to the recovery of threedimensional 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.
Björn Stenger, Arasanathan Thayananthan, Phil
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where PAMI
Authors Björn Stenger, Arasanathan Thayananthan, Philip H. S. Torr, Roberto Cipolla
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