CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
Traffic Sign Recognition Using a Multi-Task Convolutional Neural Network
Luo, Hengliang1,2; Yang, Yi1; Tong, Bei1; Wu, Fuchao1; Fan, Bin1
Source PublicationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
2018-04-01
Volume19Issue:4Pages:1100-1111
SubtypeArticle
AbstractAlthough traffic sign recognition has been studied for many years, most existing works are focused on the symbol-based traffic signs. This paper proposes a new data-driven system to recognize all categories of traffic signs, which include both symbol-based and text-based signs, in video sequences captured by a camera mounted on a car. The system consists of three stages, traffic sign regions of interest (ROIs) extraction, ROIs refinement and classification, and post-processing. Traffic sign ROIs from each frame are first extracted using maximally stable extremal regions on gray and normalized RGB channels. Then, they are refined and assigned to their detailed classes via the proposed multi-task convolutional neural network, which is trained with a large amount of data, including synthetic traffic signs and images labeled from street views. The post-processing finally combines the results in all frames to make a recognition decision. Experimental results have demonstrated the effectiveness of the proposed system.
KeywordTraffic Sign Detection Traffic Sign Classification Convolutional Neural Network Multi-task Learning
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TITS.2017.2714691
WOS KeywordCLASSIFICATION
Indexed BySCI
Language英语
Funding OrganizationNational High Technology Research and Development Program of China(2015AA124102) ; National Natural Science Foundation of China(61375043) ; Beijing Natural Science Foundation(4142057)
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000429017300009
Citation statistics
Cited Times:17[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19824
Collection模式识别国家重点实验室_机器人视觉
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Luo, Hengliang,Yang, Yi,Tong, Bei,et al. Traffic Sign Recognition Using a Multi-Task Convolutional Neural Network[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2018,19(4):1100-1111.
APA Luo, Hengliang,Yang, Yi,Tong, Bei,Wu, Fuchao,&Fan, Bin.(2018).Traffic Sign Recognition Using a Multi-Task Convolutional Neural Network.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,19(4),1100-1111.
MLA Luo, Hengliang,et al."Traffic Sign Recognition Using a Multi-Task Convolutional Neural Network".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 19.4(2018):1100-1111.
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