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Probabilistic Detection and Tracking of Motion Boundaries

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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.

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Authors and Affiliations

  1. Xerox Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CA, 94304, USA

    Michael J. Black & David J. Fleet

  2. Department of Computer Science, Brown University, Box 1910, Providence, RI, 02912, USA

    Michael J. Black

  3. Department of Computing Science, Queen's University, Kingston, K7L 3N6, Canada

    David J. Fleet

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  1. Michael J. Black
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  2. David J. Fleet
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Black, M.J., Fleet, D.J. Probabilistic Detection and Tracking of Motion Boundaries. International Journal of Computer Vision 38, 231–245 (2000). https://doi.org/10.1023/A:1008195307933

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  • DOI: https://doi.org/10.1023/A:1008195307933

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