Spatio-temporal Center-symmetric Local Derivative Patterns for Objects Detection in Video Surveillance

Marwa Jmal, Wided Souidene, Rabah Attia

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 results to the state of the art.

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Paper Citation


in Harvard Style

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


in Bibtex Style

@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},
}


in EndNote Style

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