Motion Detection Robust to Perturbations: A Statistical Regularization and Temporal Integration Framework
Abstract
The authors present a scheme for motion detection exploiting temporal integration and local contextual information. A multiscale temporal decomposition is supplied to the original sequence. Change detection is performed using a likelihood test at each temporal scale. The decision process is formalized within a statistical regularization framework and takes advantage of a tracking module. Motion detection is achieved by minimizing an energy function. This function involves three terms, expressing (1) adequacy between temporal variations at different scales and motion labels, (2) local spatial regularization, and (3) coherence between temporal prediction of change area locations and motion labels. Experimental results on real scenes are reported.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Cite
Text
Létang et al. "Motion Detection Robust to Perturbations: A Statistical Regularization and Temporal Integration Framework." IEEE/CVF International Conference on Computer Vision, 1993. doi:10.1109/ICCV.1993.378239Markdown
[Létang et al. "Motion Detection Robust to Perturbations: A Statistical Regularization and Temporal Integration Framework." IEEE/CVF International Conference on Computer Vision, 1993.](https://mlanthology.org/iccv/1993/letang1993iccv-motion/) doi:10.1109/ICCV.1993.378239BibTeX
@inproceedings{letang1993iccv-motion,
title = {{Motion Detection Robust to Perturbations: A Statistical Regularization and Temporal Integration Framework}},
author = {Létang, Jean-Michel and Rebuffel, Veronique and Bouthemy, Patrick},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {1993},
pages = {21-30},
doi = {10.1109/ICCV.1993.378239},
url = {https://mlanthology.org/iccv/1993/letang1993iccv-motion/}
}