Mean shift for accurate license plate localization

Jia, W; Zhang, H; He, X; Piccardi, M

Mean shift for accurate license plate localization

Jia, W

Zhang, H He, X

Piccardi, M

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Publication Type:: Conference Proceeding
Citation:: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2005, 2005 pp. 566 - 571
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.contributor.author	Zhang, H	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Piccardi, M https://orcid.org/0000-0001-9250-6604	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2005, 2005 pp. 566 - 571	en_US
dc.identifier.isbn	0780392159	en_US
dc.identifier.isbn	9780780392151	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2730
dc.description.abstract	This paper presents a region-based algorithm for accurate license plate localization, where mean shift is utilized to filter and segment color vehicle images into candidate regions. Three features are extracted in order to decide whether a candidate region represents a real license plate, namely, rectangularity, aspect ratio, and edge density. Then, the Mahalanobis classifier is used with respect to above three features to classify license plate regions and non-license plate regions. Experimental results show that the proposed algorithm produces high robustness and accuracy. © 2005 IEEE.	en_US
dc.relation.ispartof	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC	en_US
dc.relation.isbasedon	10.1109/ITSC.2005.1520110	en_US
dc.title	Mean shift for accurate license plate localization	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2005	en_US
utslib.for	080106 Image Processing	en_US
utslib.for	080107 Natural Language Processing	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Vienna, Austria	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2005	en_US

Abstract:

This paper presents a region-based algorithm for accurate license plate localization, where mean shift is utilized to filter and segment color vehicle images into candidate regions. Three features are extracted in order to decide whether a candidate region represents a real license plate, namely, rectangularity, aspect ratio, and edge density. Then, the Mahalanobis classifier is used with respect to above three features to classify license plate regions and non-license plate regions. Experimental results show that the proposed algorithm produces high robustness and accuracy. © 2005 IEEE.

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