|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
FILTERING USING A TREE-BASED ESTIMATOR
B. Stenger, A. Thayananthan, P. H. S. Torr, and R. Cipolla,
Within this paper a new framework for Bayesian tracking is presented, which approximates the posterior distribution at multiple resolutions. We propose a tree-based representation of the distribution, where the leaves define a partition of the state space with piecewise constant density. The advantage of this representation is that regions with low probability mass can be rapidly discarded in a hierarchical search, and the distribution can be approximated to arbitrary precision. We demonstrate the effectiveness of the technique by using it for tracking 3D articulated and non-rigid motion in front of cluttered background. More specifically, we are interested in estimating the joint angles, position and orientation of a 3D hand model in order to drive an avatar.
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