Robust Background Modeling and Foreground Detection using Dynamic Textures

M. Sami Zitouni, Harish Bhaskar, Mohammed Al-Mualla

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.

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Paper Citation


in Harvard Style

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 - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 403-410. DOI: 10.5220/0005724204030410


in Bibtex Style

@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 - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={403-410},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005724204030410},
isbn={978-989-758-175-5},
}


in EndNote Style

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