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International Journal of Computer Vision

, Volume 38, Issue 3, pp 231–245 | Cite as

Probabilistic Detection and Tracking of Motion Boundaries

  • Michael J. Black
  • David J. Fleet
Article

Abstract

We propose a Bayesian framework for representing and recognizing local image motion in terms of two basic models: translational motion and motion boundaries. Motion boundaries are represented using a non-linear generative model that explicitly encodes the orientation of the boundary, the velocities on either side, the motion of the occluding edge over time, and the appearance/disappearance of pixels at the boundary. We represent the posterior probability distribution over the model parameters given the image data using discrete samples. This distribution is propagated over time using a particle filtering algorithm. To efficiently represent such a high-dimensional space we initialize samples using the responses of a low-level motion discontinuity detector. The formulation and computational model provide a general probabilistic framework for motion estimation with multiple, non-linear, models.

motion discontinuities occlusion optical flow Bayesian methods particle filtering 

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Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Michael J. Black
    • 1
    • 2
  • David J. Fleet
    • 1
    • 3
  1. 1.Xerox Palo Alto Research CenterPalo AltoUSA
  2. 2.Department of Computer ScienceBrown UniversityProvidenceUSA
  3. 3.Department of Computing ScienceQueen's UniversityKingstonCanada

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