Vision-based traffic sign compliance evaluation using convolutional neural network

Manual monitoring of road signs compliance procedures are adapted by developing countries. As effective as this method is, the amount of time and funds needed to cover a large area is quite alarming. Thus, a need for a vision - based traffic sign detection and recognition system. However, while a ma...

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Main Authors:	Roxas, Edison A., Acilo, Joshua N., Vicerra, Ryan Rhay P., Dadios, Elmer P., Bandala, Argel A.
Format:	text
Published:	Animo Repository 2018
Subjects:	Traffic monitoring Computer vision Traffic signs and signals—Control systems Electrical and Computer Engineering Electrical and Electronics Systems and Communications
Online Access:	https://animorepository.dlsu.edu.ph/faculty_research/1552 https://animorepository.dlsu.edu.ph/context/faculty_research/article/2551/type/native/viewcontent
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Institution:	De La Salle University

id	oai:animorepository.dlsu.edu.ph:faculty_research-2551
record_format	eprints
spelling	oai:animorepository.dlsu.edu.ph:faculty_research-25512023-01-10T02:26:53Z Vision-based traffic sign compliance evaluation using convolutional neural network Roxas, Edison A. Acilo, Joshua N. Vicerra, Ryan Rhay P. Dadios, Elmer P. Bandala, Argel A. Manual monitoring of road signs compliance procedures are adapted by developing countries. As effective as this method is, the amount of time and funds needed to cover a large area is quite alarming. Thus, a need for a vision - based traffic sign detection and recognition system. However, while a majority of researches using machine vision focuses on the development of a robust real - time traffic sign recognition system, researches addressing the issue of the sign compliance and standardization is lacking. © 2018 IEEE. 2018-06-22T07:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/1552 https://animorepository.dlsu.edu.ph/context/faculty_research/article/2551/type/native/viewcontent Faculty Research Work Animo Repository Traffic monitoring Computer vision Traffic signs and signals—Control systems Electrical and Computer Engineering Electrical and Electronics Systems and Communications
institution	De La Salle University
building	De La Salle University Library
continent	Asia
country	Philippines Philippines
content_provider	De La Salle University Library
collection	DLSU Institutional Repository
topic	Traffic monitoring Computer vision Traffic signs and signals—Control systems Electrical and Computer Engineering Electrical and Electronics Systems and Communications
spellingShingle	Traffic monitoring Computer vision Traffic signs and signals—Control systems Electrical and Computer Engineering Electrical and Electronics Systems and Communications Roxas, Edison A. Acilo, Joshua N. Vicerra, Ryan Rhay P. Dadios, Elmer P. Bandala, Argel A. Vision-based traffic sign compliance evaluation using convolutional neural network
description	Manual monitoring of road signs compliance procedures are adapted by developing countries. As effective as this method is, the amount of time and funds needed to cover a large area is quite alarming. Thus, a need for a vision - based traffic sign detection and recognition system. However, while a majority of researches using machine vision focuses on the development of a robust real - time traffic sign recognition system, researches addressing the issue of the sign compliance and standardization is lacking. © 2018 IEEE.
format	text
author	Roxas, Edison A. Acilo, Joshua N. Vicerra, Ryan Rhay P. Dadios, Elmer P. Bandala, Argel A.
author_facet	Roxas, Edison A. Acilo, Joshua N. Vicerra, Ryan Rhay P. Dadios, Elmer P. Bandala, Argel A.
author_sort	Roxas, Edison A.
title	Vision-based traffic sign compliance evaluation using convolutional neural network
title_short	Vision-based traffic sign compliance evaluation using convolutional neural network
title_full	Vision-based traffic sign compliance evaluation using convolutional neural network
title_fullStr	Vision-based traffic sign compliance evaluation using convolutional neural network
title_full_unstemmed	Vision-based traffic sign compliance evaluation using convolutional neural network
title_sort	vision-based traffic sign compliance evaluation using convolutional neural network
publisher	Animo Repository
publishDate	2018
url	https://animorepository.dlsu.edu.ph/faculty_research/1552 https://animorepository.dlsu.edu.ph/context/faculty_research/article/2551/type/native/viewcontent
_version_	1754713735014907904

Vision-based traffic sign compliance evaluation using convolutional neural network

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