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Hand Tracking Using a Quadric Surface Model and Bayesian Filtering

  • Roberto Cipolla
  • Bjorn Stenger
  • Arasanathan Thayananthan
  • Philip H. S. Torr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2768)

Abstract

Within this paper a technique for model-based 3D hand tracking is presented. A hand model is built from a set of truncated quadrics, approximating the anatomy of a real hand with few parameters. Given that the projection of a quadric onto the image plane is a conic, the contours can be generated efficiently. These model contours are used as shape templates to evaluate possible matches in the current frame. The evaluation is done within a hierarchical Bayesian filtering framework, where the posterior distribution is computed efficiently using a tree of templates. We demonstrate the effectiveness of the technique by using it for tracking 3D articulated and non-rigid hand motion from monocular video sequences in front of a cluttered background.

Keywords

Joint Angle Hand Motion Leaf Level Cluttered Background Camera Centre 
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 2003

Authors and Affiliations

  • Roberto Cipolla
    • 1
  • Bjorn Stenger
    • 1
  • Arasanathan Thayananthan
    • 1
  • Philip H. S. Torr
    • 2
  1. 1.Department of EngineeringUniversity of CambridgeCambridgeUK
  2. 2.Microsoft Research Ltd.CambridgeUK

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