Car plate detection using cascaded tree-style learner based on hybrid object features

Wu, Q; Zhang, H; Jia, W; He, X; Yang, J; Hintz, T

Car plate detection using cascaded tree-style learner based on hybrid object features

Wu, Q

Zhang, H Jia, W

He, X

Yang, J Hintz, T

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006, 2006
Issue Date:: 2006-12-01

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Field	Value	Language
dc.contributor.author	Wu, Q https://orcid.org/0000-0001-5641-2483	en_US
dc.contributor.author	Zhang, H	en_US
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.contributor.author	Yang, J	en_US
dc.contributor.author	Hintz, T	en_US
dc.date.issued	2006-12-01	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006, 2006	en_US
dc.identifier.isbn	0769526888	en_US
dc.identifier.isbn	9780769526881	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2588
dc.description.abstract	Car plate detection is a key component in automatic license plate recognition system. This paper adopts an enhanced cascaded tree style learner framework for car plate detection using the hybrid object features including the simple statistical features and Harr-like features. The statistical features are useful for simplifying the process on cascade classifier. The cascaded tree-style detector design will further reduce the false alarm and the false dismissal while retaining a high detection ratio. The experimental results obtained by the proposed algorithm exhibit the encouraging performance. © 2006 IEEE.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0451666
dc.relation.ispartof	Proceedings - IEEE International Conference on Video and Signal Based Surveillance 2006, AVSS 2006	en_US
dc.relation.isbasedon	10.1109/AVSS.2006.30	en_US
dc.title	Car plate detection using cascaded tree-style learner based on hybrid object features	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Sydney, Australia	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/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
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.publication-status	Published	en_US

Abstract:

Car plate detection is a key component in automatic license plate recognition system. This paper adopts an enhanced cascaded tree style learner framework for car plate detection using the hybrid object features including the simple statistical features and Harr-like features. The statistical features are useful for simplifying the process on cascade classifier. The cascaded tree-style detector design will further reduce the false alarm and the false dismissal while retaining a high detection ratio. The experimental results obtained by the proposed algorithm exhibit the encouraging performance. © 2006 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/2588