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Motion feature detection using steerable flow fields

1998

Conference Paper

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The estimation and detection of occlusion boundaries and moving bars are important and challenging problems in image sequence analysis. Here, we model such motion features as linear combinations of steerable basis flow fields. These models constrain the interpretation of image motion, and are used in the same way as translational or affine motion models. We estimate the subspace coefficients of the motion feature models directly from spatiotemporal image derivatives using a robust regression method. From the subspace coefficients we detect the presence of a motion feature and solve for the orientation of the feature and the relative velocities of the surfaces. Our method does not require the prior computation of optical flow and recovers accurate estimates of orientation and velocity.

Author(s): Fleet, D. J. and Black, M. J. and Jepson, A. D.
Book Title: IEEE Conf. on Computer Vision and Pattern Recognition, CVPR-98
Pages: 274-281
Year: 1998
Publisher: IEEE

Department(s): Perceiving Systems
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Address: Santa Barbara, CA

Links: pdf

BibTex

@inproceedings{Fleet:IEEE:1998,
  title = {Motion feature detection using steerable flow fields},
  author = {Fleet, D. J. and Black, M. J. and Jepson, A. D.},
  booktitle = {IEEE Conf. on Computer Vision and Pattern Recognition, CVPR-98},
  pages = {274-281},
  publisher = {IEEE},
  address = {Santa Barbara, CA},
  year = {1998}
}