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Published Articles >> Table of Contents >> Abstract
Ninth IEEE International Conference on Computer Vision (ICCV'03) - Volume 2
p. 1063
Filtering Using a Tree-Based Estimator
B. Stenger, University of Cambridge
A. Thayananthan, University of Cambridge
P. H. S. Torr, Microsoft Research Ltd.
R. Cipolla, University of Cambridge
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICCV.2003.1238467
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| Abstract |
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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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 Additional Information
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Citation:
B. Stenger, A. Thayananthan, P. H. S. Torr, R. Cipolla,
"Filtering Using a Tree-Based Estimator,"
iccv,
p. 1063,
Ninth IEEE International Conference on Computer Vision (ICCV'03) - Volume 2,
2003
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