Optimized Cascade of Classifiers for People Detection using Covariance Features

Malik Souded, Francois Bremond

2013

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 Harvard Style

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 - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 820-826. DOI: 10.5220/0004304208200826

in Bibtex Style

@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 - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={820-826},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004304208200826},
isbn={978-989-8565-47-1},
}


in EndNote Style

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