Fast Deformable Model for Pedestrian Detection with Haar-like features

Chou, KP; Prasad, M; Puthal, D; Chen, PH; Vishwakarma, DK; Sundarami, S; Lin, CT; Lin, WC

Fast Deformable Model for Pedestrian Detection with Haar-like features

Chou, KP Prasad, M

Puthal, D

Chen, PH Vishwakarma, DK Sundarami, S Lin, CT

Lin, WC

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Publication Type:: Conference Proceeding
Citation:: 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, 2018, 2018-January pp. 1 - 8
Issue Date:: 2018-02-02

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Field	Value	Language
dc.contributor.author	Chou, KP	en_US
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253	en_US
dc.contributor.author	Puthal, D https://orcid.org/0000-0002-8332-278X	en_US
dc.contributor.author	Chen, PH	en_US
dc.contributor.author	Vishwakarma, DK	en_US
dc.contributor.author	Sundarami, S	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Lin, WC	en_US
dc.date.issued	2018-02-02	en_US
dc.identifier.citation	2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings, 2018, 2018-January pp. 1 - 8	en_US
dc.identifier.isbn	9781538627259	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127968
dc.description.abstract	© 2017 IEEE. This paper proposes a novel Fast Deformable Model for Pedestrian Detection (FDMPD) to detect the pedestrians efficiently and accurately in the crowded environment. Despite of multiple detection methods available, detection becomes difficult due to variety of human postures and perspectives. The proposed study is divided into two parts. First part trains six Adaboost classifiers with Haar-like feature for different body parts (e.g., head, shoulders, and knees) to build the response feature maps. Second part uses these six response feature maps with full-body model to produce spatial deep features. The combined deep features are used as an input to SVM to judge the existence of pedestrian. As per the experiments conducted on the INRIA person dataset, the proposed FDMPD approach shows greater than 44.75 % improvement compared to other state-of-the-art methods in terms of efficiency and robustness.	en_US
dc.relation.ispartof	2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings	en_US
dc.relation.isbasedon	10.1109/SSCI.2017.8280931	en_US
dc.title	Fast Deformable Model for Pedestrian Detection with Haar-like features	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-January	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	2018-January	en_US

Abstract:

© 2017 IEEE. This paper proposes a novel Fast Deformable Model for Pedestrian Detection (FDMPD) to detect the pedestrians efficiently and accurately in the crowded environment. Despite of multiple detection methods available, detection becomes difficult due to variety of human postures and perspectives. The proposed study is divided into two parts. First part trains six Adaboost classifiers with Haar-like feature for different body parts (e.g., head, shoulders, and knees) to build the response feature maps. Second part uses these six response feature maps with full-body model to produce spatial deep features. The combined deep features are used as an input to SVM to judge the existence of pedestrian. As per the experiments conducted on the INRIA person dataset, the proposed FDMPD approach shows greater than 44.75 % improvement compared to other state-of-the-art methods in terms of efficiency and robustness.

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http://hdl.handle.net/10453/127968