Fast and robust road sign detection in driver assistance systems

Zhang, T; Zou, J; Jia, W

Fast and robust road sign detection in driver assistance systems

Zhang, T Zou, J Jia, W

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Publication Type:: Journal Article
Citation:: Applied Intelligence, 2018, 48 (11), pp. 4113 - 4127
Issue Date:: 2018-11-01

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Field	Value	Language
dc.contributor.author	Zhang, T	en_US
dc.contributor.author	Zou, J	en_US
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.date.issued	2018-11-01	en_US
dc.identifier.citation	Applied Intelligence, 2018, 48 (11), pp. 4113 - 4127	en_US
dc.identifier.issn	0924-669X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/130926
dc.description.abstract	© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Road sign detection plays a critical role in automatic driver assistance systems. Road signs possess a number of unique visual qualities in images due to their specific colors and symmetric shapes. In this paper, road signs are detected by a two-level hierarchical framework that considers both color and shape of the signs. To address the problem of low image contrast, we propose a new color visual saliency segmentation algorithm, which uses the ratios of enhanced and normalized color values to capture color information. To improve computation efficiency and reduce false alarm rate, we modify the fast radial symmetry transform (RST) algorithm, and propose to use an edge pairwise voting scheme to group feature points based on their underlying symmetry in the candidate regions. Experimental results on several benchmarking datasets demonstrate the superiority of our method over the state-of-the-arts on both efficiency and robustness.	en_US
dc.relation.ispartof	Applied Intelligence	en_US
dc.relation.isbasedon	10.1007/s10489-018-1199-x	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Fast and robust road sign detection in driver assistance systems	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.citation.volume	48	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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	open_access	*
pubs.issue	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	48	en_US

Abstract:

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Road sign detection plays a critical role in automatic driver assistance systems. Road signs possess a number of unique visual qualities in images due to their specific colors and symmetric shapes. In this paper, road signs are detected by a two-level hierarchical framework that considers both color and shape of the signs. To address the problem of low image contrast, we propose a new color visual saliency segmentation algorithm, which uses the ratios of enhanced and normalized color values to capture color information. To improve computation efficiency and reduce false alarm rate, we modify the fast radial symmetry transform (RST) algorithm, and propose to use an edge pairwise voting scheme to group feature points based on their underlying symmetry in the candidate regions. Experimental results on several benchmarking datasets demonstrate the superiority of our method over the state-of-the-arts on both efficiency and robustness.

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http://hdl.handle.net/10453/130926