Multi-Resolution Dynamic Mode Decomposition for Foreground/Background Separation and Object Tracking

J. Nathan Kutz, Xing Fu, Steve L. Brunton, N. Benjamin Erichson; Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, 2015, pp. 81-89

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.

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[bibtex]
@InProceedings{Kutz_2015_ICCV_Workshops,
author = {Nathan Kutz, J. and Fu, Xing and Brunton, Steve L. and Benjamin Erichson, N.},
title = {Multi-Resolution Dynamic Mode Decomposition for Foreground/Background Separation and Object Tracking},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {December},
year = {2015}
}