Issue No. 09 - September (2006 vol. 28)
Bj? Stenger , IEEE
Philip H.S. Torr , IEEE
Roberto Cipolla , IEEE
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.
B. Stenger, A. Thayananthan, R. Cipolla and P. H. Torr, "Model-Based Hand Tracking Using a Hierarchical Bayesian Filter," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 1372-1384, 2006.