Optimized Cascade of Classifiers for People Detection using Covariance Features

Malik Souded; Francois Bremond

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Paper

Optimized Cascade of Classifiers for People Detection using Covariance Features

Topics: Object Detection and Localization

In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, 820-826, 2013 , Barcelona, Spain

Authors: Malik Souded ¹ and Francois Bremond ²

Affiliations: ¹ INRIA Sophia Antipolis - Méditerranée and Digital Barriers France, France ; ² INRIA Sophia Antipolis - Méditerranée, France

Keyword(s): People Detection, Covariance Descriptor, LogitBoost.

Abstract: People detection on static images and video sequences is a critical task in many computer vision applications, like image retrieval and video surveillance. It is also one of most challenging task due to the large number of possible situations, including variations in people appearance and poses. The proposed approach optimizes an existing approach based on classification on Riemannian manifolds using covariance matrices in a boosting scheme, making training and detection faster while maintaining equivalent performances. This optimisation is achieved by clustering negative samples before training, providing a smaller number of cascade levels and less weak classifiers in most levels in comparison with the original approach. Our work was evaluated and validated on INRIA Person dataset.

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Paper citation in several formats:

Souded, M. and Bremond, F. (2013). Optimized Cascade of Classifiers for People Detection using Covariance Features. In Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP; ISBN 978-989-8565-47-1; ISSN 2184-4321, SciTePress, pages 820-826. DOI: 10.5220/0004304208200826

@conference{visapp13,
author={Malik Souded. and Francois Bremond.},
title={Optimized Cascade of Classifiers for People Detection using Covariance Features},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP},
year={2013},
pages={820-826},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004304208200826},
isbn={978-989-8565-47-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the International Conference on Computer Vision Theory and Applications (VISIGRAPP 2013) - Volume 1: VISAPP
TI - Optimized Cascade of Classifiers for People Detection using Covariance Features
SN - 978-989-8565-47-1
IS - 2184-4321
AU - Souded, M.
AU - Bremond, F.
PY - 2013
SP - 820
EP - 826
DO - 10.5220/0004304208200826
PB - SciTePress