Issue No. 09 - September (2006 vol. 28)
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
Bayesian methods, Particle tracking, Image sequences, Particle filters, State-space methods, Filtering, Video sequences, Parameter estimation, Robustness, Target tracking
B. Stenger, A. Thayananthan, P. Torr and R. Cipolla, "Model-based hand tracking using a hierarchical Bayesian filter," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. 9, pp. 1372-1384, 2009.