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International Journal of Computer Vision

, Volume 26, Issue 1, pp 63–84 | Cite as

EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation

  • Michael J. Black
  • Allan D. Jepson
Article

Abstract

This paper describes an approach for tracking rigid and articulated objects using a view-based representation. The approach builds on and extends work on eigenspace representations, robust estimation techniques, and parameterized optical flow estimation. First, we note that the least-squares image reconstruction of standard eigenspace techniques has a number of problems and we reformulate the reconstruction problem as one of robust estimation. Second we define a “subspace constancy assumption” that allows us to exploit techniques for parameterized optical flow estimation to simultaneously solve for the view of an object and the affine transformation between the eigenspace and the image. To account for large affine transformations between the eigenspace and the image we define a multi-scale eigenspace representation and a coarse-to-fine matching strategy. Finally, we use these techniques to track objects over long image sequences in which the objects simultaneously undergo both affine image motions and changes of view. In particular we use this “EigenTracking” technique to track and recognize the gestures of a moving hand.

eigenspace methods robust estimation view-based representations gesture recognition parametric models of optical flow tracking object recognition motion analysis 

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Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • Michael J. Black
    • 1
  • Allan D. Jepson
    • 2
  1. 1.Xerox Palo Alto Research CenterPalo Alto
  2. 2.Department of Computer ScienceUniversity of TorontoTorontoCanada

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