Multi-Resolution Dynamic Mode Decomposition for Foreground/Background Separation and Object Tracking
Abstract
We demonstrate that the integration of the recently developed dynamic mode decomposition with a multi-resolution analysis allows for a decomposition of video streams into multi-time scale features and objects. A one-level separation allows for background (low-rank) and foreground (sparse) separation of the video, or robust principal component analysis. Further iteration of the method allows a video data set to be separated into objects moving at different rates against the slowly varying background, thus allowing for multiple-target tracking and detection. The algorithm is computationally efficient and can be integrated with many further innovations including compressive sensing architectures and GPU algorithms.
Cite
Text
Kutz et al. "Multi-Resolution Dynamic Mode Decomposition for Foreground/Background Separation and Object Tracking." IEEE/CVF International Conference on Computer Vision Workshops, 2015. doi:10.1109/ICCVW.2015.122Markdown
[Kutz et al. "Multi-Resolution Dynamic Mode Decomposition for Foreground/Background Separation and Object Tracking." IEEE/CVF International Conference on Computer Vision Workshops, 2015.](https://mlanthology.org/iccvw/2015/kutz2015iccvw-multiresolution/) doi:10.1109/ICCVW.2015.122BibTeX
@inproceedings{kutz2015iccvw-multiresolution,
title = {{Multi-Resolution Dynamic Mode Decomposition for Foreground/Background Separation and Object Tracking}},
author = {Kutz, J. Nathan and Fu, Xing and Brunton, Steven L. and Erichson, N. Benjamin},
booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
year = {2015},
pages = {921-929},
doi = {10.1109/ICCVW.2015.122},
url = {https://mlanthology.org/iccvw/2015/kutz2015iccvw-multiresolution/}
}