Learned Temporal Models of Image Motion
Abstract
An approach for learning and estimating temporal-flow models from image sequences is proposed. The temporal-flow models are represented as a set of orthogonal temporal-flow bases that are learned using principal component analysis of instantaneous flow measurements. Spatial constraints on the temporal-flow are also developed for modeling the motion of regions in rigid and coordinated motion. The performance of these models is demonstrated on several long image sequences of rigid and articulated bodies in motion.
Cite
Text
Yacoob and Davis. "Learned Temporal Models of Image Motion." IEEE/CVF International Conference on Computer Vision, 1998. doi:10.1109/ICCV.1998.710757Markdown
[Yacoob and Davis. "Learned Temporal Models of Image Motion." IEEE/CVF International Conference on Computer Vision, 1998.](https://mlanthology.org/iccv/1998/yacoob1998iccv-learned/) doi:10.1109/ICCV.1998.710757BibTeX
@inproceedings{yacoob1998iccv-learned,
title = {{Learned Temporal Models of Image Motion}},
author = {Yacoob, Yaser and Davis, Larry S.},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {1998},
pages = {446-453},
doi = {10.1109/ICCV.1998.710757},
url = {https://mlanthology.org/iccv/1998/yacoob1998iccv-learned/}
}