Probabilistic detection and tracking of motion boundaries
2000
Article
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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.
Author(s): | Black, M. J. and Fleet, D. J. |
Journal: | Int. J. of Computer Vision |
Volume: | 38 |
Number (issue): | 3 |
Pages: | 231-245 |
Year: | 2000 |
Month: | July |
Department(s): | Perceiving Systems |
Bibtex Type: | Article (article) |
Paper Type: | Journal |
DOI: | 10.1023/A%3A1008195307933 |
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BibTex @article{Black:IJCV:2000, title = {Probabilistic detection and tracking of motion boundaries}, author = {Black, M. J. and Fleet, D. J.}, journal = {Int. J. of Computer Vision}, volume = {38}, number = {3}, pages = {231-245}, month = jul, year = {2000}, doi = {10.1023/A%3A1008195307933}, month_numeric = {7} } |