Multi-view Vehicle Detection based on Part Model with Active Learning

Prasad, M; Liu, CL; Li, DL; Jha, C; Lin, CT

Multi-view Vehicle Detection based on Part Model with Active Learning

Prasad, M

Liu, CL Li, DL Jha, C Lin, CT

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2018, 2018-July
Issue Date:: 2018-10-10

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Field	Value	Language
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253	en_US
dc.contributor.author	Liu, CL	en_US
dc.contributor.author	Li, DL	en_US
dc.contributor.author	Jha, C	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.date.available	2020-10-11T18:03:46Z
dc.date.issued	2018-10-10	en_US
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2018, 2018-July	en_US
dc.identifier.isbn	9781509060146	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129733
dc.description.abstract	© 2018 IEEE. Nowadays, most ofthe vehicle detection methods aim to detect only single-view vehicles, and the performance is easily affected by partial occlusion. Therefore, a novel multi-view vehicle detection system is proposed to solve the problem of partial occlusion. The proposed system is divided into two steps: Background filtering and part model. Background filtering step is used to filter out trees, sky and other road background objects. In the part model step, each of the part models is trained by samples collected by using the proposed active learning algorithm. This paper validates the performance of the background filtering method and the part model algorithm in multi-view car detection. The performance of the proposed method outperforms previously proposed methods.	en_US
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2018.8489671	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Multi-view Vehicle Detection based on Part Model with Active Learning	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2018-July	en_US
utslib.for	0805 Distributed Computing	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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	2018-July	en_US

Abstract:

© 2018 IEEE. Nowadays, most ofthe vehicle detection methods aim to detect only single-view vehicles, and the performance is easily affected by partial occlusion. Therefore, a novel multi-view vehicle detection system is proposed to solve the problem of partial occlusion. The proposed system is divided into two steps: Background filtering and part model. Background filtering step is used to filter out trees, sky and other road background objects. In the part model step, each of the part models is trained by samples collected by using the proposed active learning algorithm. This paper validates the performance of the background filtering method and the part model algorithm in multi-view car detection. The performance of the proposed method outperforms previously proposed methods.

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http://hdl.handle.net/10453/129733