PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Multivariate relevance vector machines for tracking
A. Thayananthan, R. Navaratnam, B. Stenger, Philip Torr and R. Cipolla
In: ECCV 2006, 7-13 May 2006, Graz, Austria.

Abstract

This paper presents a learning based approach to tracking articulated human body motion from a single camera. In order to address the problem of pose ambiguity, a one-to-many mapping from image feature to state space is learned using a set of relevance vector machines, extended to handle multivariate outputs. The image features are Hausdorff matching scores obtained by matching different shape templates to the image, where the multivariate relevance vector machines (MVRVM) select a sparse set of these templates. We demonstrate that these Hausdorff features reduce the estimation error in clutter compared to shape-context histograms. The method is applied to the pose estimation problem from a single input frame, and is embedded within a probabilistic tracking framework to include temporal information. We apply the algorithm to 3D hand tracking and full human body tracking.

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EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:2110
Deposited By:Mudigonda Pawan Kumar
Deposited On:21 May 2006