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Robust dynamic motion estimation over time
Abstract
A novel approach to incrementally estimating visual motion over a sequence of images is presented. The authors start by formulating constraints on image motion to account for the possibility of multiple motions. This is achieved by exploiting the notions of weak continuity and robust statistics in the formulation of a minimization problem. The resulting objective function is non-convex. Traditional stochastic relaxation techniques for minimizing such functions prove inappropriate for the task. A highly parallel incremental stochastic minimization algorithm is presented which has a number of advantages over previous approaches. The incremental nature of the scheme makes it dynamic and permits the detection of occlusion and disocclusion boundaries.>
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Research works printed but not linked: 11
- black, 1990, Constraints for the early detection of discontinuity from motion, Proc AAAI-90, 1060
- 10.1109/ICCV.1990.139487 · doi:10.1109/iccv.1990.139487
- konrad, 1989, Bayesian estimation of motion fields from image sequences
- 10.1007/BF00133032 · DOI · Paper page
- blake, 1987, Visual Reconstruction, 10.7551/mitpress/7132.001.0001 · DOI · Paper page
- 10.1016/0021-9991(84)90095-0 · DOI · Paper page
- hampel, 1986, Robust Statistics The Approach Based on Influence Functions
- 10.1109/TPAMI.1984.4767596 · DOI · Paper page
- black, 1990, Robust dyanamic motion estimation over time
- 10.1109/ICCV.1990.139534 · doi:10.1109/iccv.1990.139534
- 10.1007/BF00158167 · DOI · Paper page
Cited by
A Database and Evaluation Methodology for Optical Flow
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205
“Given more than two frames in the video, it is also possible to add temporal smoothness terms ∂u ∂t and ∂v ∂t to Equation (9) [10, 49, 54].”
BackgroundMethodSelf-citationLarge Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
Brox, T., & Malik, J. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.143 · Cited by 1,295
Lucas/Kanade meets Horn/Schunck : combining local and global optic flow methods
Weickert, J., Bruhn, A., & Schnörr, C. (2003) · doi:10.22028/d291-26234 · Cited by 963
A Database and Evaluation Methodology for Optical Flow
Baker, S., Roth, S., Scharstein, D., Black, M. J., Lewis, J. P., & Szeliski, R. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408903 · Cited by 618
Self-citationHighly Accurate Optic Flow Computation with Theoretically Justified Warping
Papenberg, N., Bruhn, A., Brox, T., Didas, S., & Weickert, J. (2006) · International Journal of Computer Vision · doi:10.1007/s11263-005-3960-y · Cited by 409
Metadata
- Authors
- Black, M. J., & Anandan, P.
- Venue
- Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE Comput. Sco. Press
- Published
- 2002-12-10
- Pages
- 296-302
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2157719643
- Cited by
- 241
- References
- 0
- FWCI
- 8.65
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Black, M. J., & Anandan, P. (2002). Robust dynamic motion estimation over time. Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.1991.139705
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