Learning global and local features for license plate detection

Wang, S; Jia, W; Wu, Q; He, X; Yang, J

Learning global and local features for license plate detection

Wang, S Jia, W

Wu, Q

He, X

Yang, J

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 7064 LNCS (PART 3), pp. 547 - 556
Issue Date:: 2011-11-28

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Field	Value	Language
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Yang, J	en_US
dc.date.issued	2011-11-28	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011, 7064 LNCS (PART 3), pp. 547 - 556	en_US
dc.identifier.isbn	9783642249648	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/29563
dc.description.abstract	This paper proposes an intelligent system that is capable of automatically detecting license plates from static images captured by a digital still camera. A supervised learning approach is used to extract features from license plates, and both global feature and local feature are organized into a cascaded structure. In general, our framework can be divided into two stages. The first stage is constructed by extracting global correlation features and a posterior probability can be estimated to quickly determine the degree of resemblance between the evaluated image region and a license plate. The second stage is constructed by further extracting local dense-SIFT (dSIFT) features for AdaBoost supervised learning approach, and the selected dSIFT features will be used to construct a strong classifier. Using dSIFT as a type of highly distinctive local feature, our algorithm gives high detection rate under various complex conditions. The proposed framework is compared with existing works and promising results are obtained. © 2011 Springer-Verlag.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-642-24965-5_62	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Learning global and local features for license plate detection	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 3	en_US
utslib.citation.volume	7064 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Shanghai / China	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
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	closed_access
pubs.issue	PART 3	en_US
pubs.publication-status	Published	en_US
pubs.volume	7064 LNCS	en_US

Abstract:

This paper proposes an intelligent system that is capable of automatically detecting license plates from static images captured by a digital still camera. A supervised learning approach is used to extract features from license plates, and both global feature and local feature are organized into a cascaded structure. In general, our framework can be divided into two stages. The first stage is constructed by extracting global correlation features and a posterior probability can be estimated to quickly determine the degree of resemblance between the evaluated image region and a license plate. The second stage is constructed by further extracting local dense-SIFT (dSIFT) features for AdaBoost supervised learning approach, and the selected dSIFT features will be used to construct a strong classifier. Using dSIFT as a type of highly distinctive local feature, our algorithm gives high detection rate under various complex conditions. The proposed framework is compared with existing works and promising results are obtained. © 2011 Springer-Verlag.

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http://hdl.handle.net/10453/29563