A Time Efficient Model for Region of Interest Extraction in Real Time Traffic Signs Recognition System

Qararyah, F; Daraghmi, YA; Daraghmi, E; Rajora, S; Lin, CT; Prasad, M

A Time Efficient Model for Region of Interest Extraction in Real Time Traffic Signs Recognition System

Qararyah, F Daraghmi, YA Daraghmi, E Rajora, S Lin, CT

Prasad, M

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, 2019, pp. 83 - 87
Issue Date:: 2019-01-28

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Field	Value	Language
dc.contributor.author	Qararyah, F	en_US
dc.contributor.author	Daraghmi, YA	en_US
dc.contributor.author	Daraghmi, E	en_US
dc.contributor.author	Rajora, S	en_US
dc.contributor.author	Lin, CT https://orcid.org/0000-0001-8371-8197	en_US
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253	en_US
dc.date.issued	2019-01-28	en_US
dc.identifier.citation	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, 2019, pp. 83 - 87	en_US
dc.identifier.isbn	9781538692769	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132144
dc.description.abstract	© 2018 IEEE. Computation intelligence plays a major role in developing intelligent vehicles, which contains a Traffic Sign Recognition (TSR) system for increasing vehicle safety. Traffic sign recognition systems consist of an initial phase called Traffic Sign Detection (TSD), where images and colors are segmented and fed to the recognition phase. The most challenging process in TSR systems in terms of time consumption is the detection phase. The previous studies proposed different models for traffic sign detection, however, the computation time of these models still requires improvement for enabling real time systems. Therefore, this paper focuses on the computational time and proposes a novel time efficient color segmentation model based on logistic regression. This paper uses RGB color space as the domain to extract the features of our hypothesis; this has boosted the speed of the proposed model, since no color conversion is needed. The trained segmentation classifier is tested on 1000 traffic sign images taken in different lighting conditions. The experimental results show that the proposed model segmented 974 of these images correctly and in a time less than one-fifth of the time needed by any other robust segmentation methods.	en_US
dc.relation.ispartof	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018	en_US
dc.relation.isbasedon	10.1109/SSCI.2018.8628874	en_US
dc.title	A Time Efficient Model for Region of Interest Extraction in Real Time Traffic Signs Recognition System	en_US
dc.type	Conference Proceeding
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	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. Computation intelligence plays a major role in developing intelligent vehicles, which contains a Traffic Sign Recognition (TSR) system for increasing vehicle safety. Traffic sign recognition systems consist of an initial phase called Traffic Sign Detection (TSD), where images and colors are segmented and fed to the recognition phase. The most challenging process in TSR systems in terms of time consumption is the detection phase. The previous studies proposed different models for traffic sign detection, however, the computation time of these models still requires improvement for enabling real time systems. Therefore, this paper focuses on the computational time and proposes a novel time efficient color segmentation model based on logistic regression. This paper uses RGB color space as the domain to extract the features of our hypothesis; this has boosted the speed of the proposed model, since no color conversion is needed. The trained segmentation classifier is tested on 1000 traffic sign images taken in different lighting conditions. The experimental results show that the proposed model segmented 974 of these images correctly and in a time less than one-fifth of the time needed by any other robust segmentation methods.

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http://hdl.handle.net/10453/132144