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Authors: Lu Wang ; Xiaoli Ji ; Qingxu Deng and Mingxing Jia

Affiliation: Northeastern University, China

ISBN: 978-989-758-004-8

Keyword(s): Deformable Part-based Model, Multiple Pedestrian Detection, Crowd Detection, Video Surveillance.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Motion, Tracking and Stereo Vision ; Video Surveillance and Event Detection

Abstract: Pedestrian detection is a challenging task for video surveillance. The problem becomes more difficult when occlusion is prevalent. In this paper, we extend a deformable part-based pedestrian detector to pedestrian detection in crowded scenes by considering both body part detection responses and detections' mutual spatial relationship. Specifically, we first decompose the full body detector into several body part detectors, whose detection responses can be computed efficiently from the response of the full body detector. Then, given the detection responses of the body part detectors, hypotheses are nominated by considering both detection scores and responses’ mutual spatial relationship. Finally, a local optimization process is applied to make the final decision, where an objective function encouraging detections with high confidence, high discriminability and low conflict with other detections is proposed to select the best candidate detections. Experimental results show the effective ness of the proposed approach. (More)

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Paper citation in several formats:
Wang, L.; Ji, X.; Deng, Q. and Jia, M. (2014). Deformable Part Model based Multiple Pedestrian Detection for Video Surveillance in Crowded Scenes.In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 599-604. DOI: 10.5220/0004739105990604

@conference{visapp14,
author={Lu Wang. and Xiaoli Ji. and Qingxu Deng. and Mingxing Jia.},
title={Deformable Part Model based Multiple Pedestrian Detection for Video Surveillance in Crowded Scenes},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={599-604},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004739105990604},
isbn={978-989-758-004-8},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - Deformable Part Model based Multiple Pedestrian Detection for Video Surveillance in Crowded Scenes
SN - 978-989-758-004-8
AU - Wang, L.
AU - Ji, X.
AU - Deng, Q.
AU - Jia, M.
PY - 2014
SP - 599
EP - 604
DO - 10.5220/0004739105990604

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