Dynamic mode decomposition via dictionary learning for foreground modeling in videos

Israr Ul Haq, Keisuke Fujii, Yoshinobu Kawahara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationVISAPP
EditorsGiovanni Maria Farinella, Petia Radeva, Jose Braz
PublisherSciTePress
Pages476-483
Number of pages8
ISBN (Electronic)9789897584022
Publication statusPublished - Jan 1 2020
Event15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020 - Valletta, Malta
Duration: Feb 27 2020Feb 29 2020

Publication series

NameVISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Volume5

Conference

Conference15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020
CountryMalta
CityValletta
Period2/27/202/29/20

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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  • Cite this

    Haq, I. U., Fujii, K., & Kawahara, Y. (2020). Dynamic mode decomposition via dictionary learning for foreground modeling in videos. In G. M. Farinella, P. Radeva, & J. Braz (Eds.), VISAPP (pp. 476-483). (VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications; Vol. 5). SciTePress.