Dynamic Mode Decomposition via Dictionary Learning for Foreground Modeling in Videos

Israr Haq, Keisuke Fujii, Yoshinobu Kawahara

Abstract

Accurate extraction of foregrounds in videos is one of the challenging problems in computer vision. In this study, we propose dynamic mode decomposition via dictionary learning (dl-DMD), which is applied to extract moving objects by separating the sequence of video frames into foreground and background information with a dictionary learned using block patches on the video frames. Dynamic mode decomposition (DMD) decomposes spatiotemporal data into spatial modes, each of whose temporal behavior is characterized by a single frequency and growth/decay rate and is applicable to split a video into foregrounds and the background when applying it to a video. And, in dl-DMD, DMD is applied on coefficient matrices estimated over a learned dictionary, which enables accurate estimation of dynamical information in videos. Due to this scheme, dl-DMD can analyze the dynamics of respective regions in a video based on estimated amplitudes and temporal evolution over patches. The results on synthetic data exhibit that dl-DMD outperforms the standard DMD and compressed DMD (cDMD) based methods. Also, the results of an empirical performance evaluation in the case of foreground extraction from videos using publicly available dataset demonstrates the effectiveness of the proposed dl-DMD algorithm and achieves a performance that is comparable to that of the state-of-the-art techniques in foreground extraction tasks.

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Paper Citation


in Harvard Style

Haq I., Fujii K. and Kawahara Y. (2020). Dynamic Mode Decomposition via Dictionary Learning for Foreground Modeling in Videos.In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-402-2, pages 476-483. DOI: 10.5220/0009144604760483


in Bibtex Style

@conference{visapp20,
author={Israr Haq and Keisuke Fujii and Yoshinobu Kawahara},
title={Dynamic Mode Decomposition via Dictionary Learning for Foreground Modeling in Videos},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2020},
pages={476-483},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009144604760483},
isbn={978-989-758-402-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Dynamic Mode Decomposition via Dictionary Learning for Foreground Modeling in Videos
SN - 978-989-758-402-2
AU - Haq I.
AU - Fujii K.
AU - Kawahara Y.
PY - 2020
SP - 476
EP - 483
DO - 10.5220/0009144604760483