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

Authors
  • Black, Michael J.1
  • Jepson, Allan D.2
  • 1 Xerox Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CA, 94304 , Palo Alto
  • 2 University of Toronto, Department of Computer Science, Toronto, Ontario, M5S 3H5, Canada , Toronto
Type
Published Article
Journal
International Journal of Computer Vision
Publisher
Springer-Verlag
Publication Date
Jan 01, 1998
Volume
26
Issue
1
Pages
63–84
Identifiers
DOI: 10.1023/A:1007939232436
Source
Springer Nature
Keywords
License
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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.

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