Joint Motion Estimation and Segmentation of Complex Scenes with Label Costs and Occlusion Modeling
Abstract
We propose a unified variational formulation for joint motion estimation and segmentation with explicit occlusion handling. This is done by a multi-label representation of the flow field, where each label corresponds to a parametric representation of the motion. We use a convex formulation of the multi-label Potts model with label costs and show that the asymmetric map-uniqueness criterion can be integrated into our formulation by means of convex constraints. Explicit occlusion handling eliminates errors otherwise created by the regularization. As occlusions can occur only at object boundaries, a large number of objects may be required. By using a fast primal-dual algorithm we are able to handle several hundred motion segments. Results are shown on several classical motion segmentation and optical flow examples.
Cite
Text
Unger et al. "Joint Motion Estimation and Segmentation of Complex Scenes with Label Costs and Occlusion Modeling." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012. doi:10.1109/CVPR.2012.6247887Markdown
[Unger et al. "Joint Motion Estimation and Segmentation of Complex Scenes with Label Costs and Occlusion Modeling." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012.](https://mlanthology.org/cvpr/2012/unger2012cvpr-joint/) doi:10.1109/CVPR.2012.6247887BibTeX
@inproceedings{unger2012cvpr-joint,
title = {{Joint Motion Estimation and Segmentation of Complex Scenes with Label Costs and Occlusion Modeling}},
author = {Unger, Markus and Werlberger, Manuel and Pock, Thomas and Bischof, Horst},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2012},
pages = {1878-1885},
doi = {10.1109/CVPR.2012.6247887},
url = {https://mlanthology.org/cvpr/2012/unger2012cvpr-joint/}
}