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Multivariate Relevance Vector Machines for Tracking

  • Arasanathan Thayananthan
  • Ramanan Navaratnam
  • Björn Stenger
  • Philip H. S. Torr
  • Roberto Cipolla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3953)

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 features 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.

Keywords

Mapping Function Relevance Vector Machine Shape Template Human Body Motion Multivariate Output 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Arasanathan Thayananthan
    • 1
  • Ramanan Navaratnam
    • 1
  • Björn Stenger
    • 2
  • Philip H. S. Torr
    • 3
  • Roberto Cipolla
    • 1
  1. 1.University of CambridgeUK
  2. 2.Toshiba Corporate R&D CenterKawasakiJapan
  3. 3.Oxford Brookes UniversityUK

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