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Authors: Marwa Jmal 1 ; Wided Souidene 2 and Rabah Attia 2

Affiliations: 1 Ecole Polytechnique de Tunisie and Telnet Innovation Labs, Tunisia ; 2 Ecole Polytechnique de Tunisie, Tunisia

ISBN: 978-989-758-175-5

Keyword(s): Local Derivative Patterns, Spatio-temporal Features, Background Modeling, Background Subtraction.

Related Ontology Subjects/Areas/Topics: Color and Texture Analyses ; Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis ; Image Formation and Preprocessing ; Image Generation Pipeline: Algorithms and Techniques

Abstract: Nowadays, more attention is being focused on background subtraction methods regarding their importance in many computer vision applications. Most of the proposed approaches are classified as pixel-based due to their low complexity and processing speed. Other methods are considered as spatiotemporal-based as they consider the surroundings of each analyzed pixel. In this context, we propose a new texture descriptor that is suitable for this task. We benefit from the advantages of local binary patterns variants to introduce a novel spatio-temporal center-symmetric local derivative patterns (STCS-LDP). Several improvements and restrictions are set in the neighboring pixels comparison level, to make the descriptor less sensitive to noise while maintaining robustness to illumination changes. We also present a simple background subtraction algorithm which is based on our STCS-LDP descriptor. Experiments on multiple video sequences proved that our method is efficient and produces comparable r esults to the state of the art. (More)

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Paper citation in several formats:
Jmal, M.; Souidene, W. and Attia, R. (2016). Spatio-temporal Center-symmetric Local Derivative Patterns for Objects Detection in Video Surveillance.In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 215-220. DOI: 10.5220/0005787702150220

@conference{visapp16,
author={Marwa Jmal. and Wided Souidene. and Rabah Attia.},
title={Spatio-temporal Center-symmetric Local Derivative Patterns for Objects Detection in Video Surveillance},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={215-220},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005787702150220},
isbn={978-989-758-175-5},
}

TY - CONF

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Spatio-temporal Center-symmetric Local Derivative Patterns for Objects Detection in Video Surveillance
SN - 978-989-758-175-5
AU - Jmal, M.
AU - Souidene, W.
AU - Attia, R.
PY - 2016
SP - 215
EP - 220
DO - 10.5220/0005787702150220

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