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 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.

• Type: Conference
• Year: 2003
• Where: ICCV
• Authors: Bjoern Stenger, Arasanathan Thayananthan, Philip H. S. Torr, Roberto Cipolla
• Keywords: Bayesian Tracking, Computer Vision, ICCV 2003, Low Probability Mass, Non-rigid Motion, Piecewise Constant Density, Posterior Distribution