Black, M.J. and Fleet, D.J.
Probabilistic detection and tracking of motion boundaries.
International Journal of Computer Vision, 38(3):231-245, 2000
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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