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Authors: M. Sami Zitouni ; Harish Bhaskar and Mohammed Al-Mualla

Affiliation: Khalifa University of Science and Technology and Research, United Arab Emirates

Keyword(s): Background Modeling, Foreground Detection, Dynamic Texture, Gaussian Mixture Model.

Abstract: In this paper, a dynamic background modeling and hence foreground detection technique using a Gaussian Mixture Model (GMM) of spatio-temporal patches of dynamic texture (DT) is proposed. Existing methods for background modeling cannot adequately distinguish movements in both background and foreground, that usually characterizes any dynamic scene. Therefore, in most of these methods, the separation of the background from foreground requires precise tuning of parameters or an apriori model of the foreground. The proposed method aims to differentiate between global from local motion by attributing the video using spatio-temporal patches of DT modeled using a typical GMM framework. In addition to alleviating the aforementioned limitations, the proposed method can cope with complex dynamic scenes without the need for training or parameter tuning. Qualitative and quantitative analysis of the method compared against competing baselines have demonstrated the superiority of the method and the robustness against dynamic variations in the background. (More)

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Paper citation in several formats:
Zitouni, M.; Bhaskar, H. and Al-Mualla, M. (2016). Robust Background Modeling and Foreground Detection using Dynamic Textures. In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP; ISBN 978-989-758-175-5; ISSN 2184-4321, SciTePress, pages 403-410. DOI: 10.5220/0005724204030410

@conference{visapp16,
author={M. Sami Zitouni. and Harish Bhaskar. and Mohammed Al{-}Mualla.},
title={Robust Background Modeling and Foreground Detection using Dynamic Textures},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP},
year={2016},
pages={403-410},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005724204030410},
isbn={978-989-758-175-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP
TI - Robust Background Modeling and Foreground Detection using Dynamic Textures
SN - 978-989-758-175-5
IS - 2184-4321
AU - Zitouni, M.
AU - Bhaskar, H.
AU - Al-Mualla, M.
PY - 2016
SP - 403
EP - 410
DO - 10.5220/0005724204030410
PB - SciTePress

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