Filtering using a tree-based estimator

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 nonrigid 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.