Probabilistic Detection and Tracking of Motion Boundaries
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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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- Probabilistic Detection and Tracking of Motion Boundaries
International Journal of Computer Vision
Volume 38, Issue 3 , pp 231-245
- Cover Date
- Print ISSN
- Online ISSN
- Kluwer Academic Publishers
- Additional Links
- motion discontinuities
- optical flow
- Bayesian methods
- particle filtering
- Industry Sectors
- Author Affiliations
- 1. Xerox Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CA, 94304, USA
- 2. Department of Computer Science, Brown University, Box 1910, Providence, RI, 02912, USA
- 3. Department of Computing Science, Queen's University, Kingston, K7L 3N6, Canada