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Towards Real-Time Traffic Sign Detection and Classification
Yang, Yi1; Luo, Hengliang1; Xu, Huarong2; Wu, Fuchao1
Source PublicationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
2016-07-01
Volume17Issue:7Pages:2022-2031
SubtypeArticle
AbstractTraffic sign recognition plays an important role in driver assistant systems and intelligent autonomous vehicles. Its real-time performance is highly desirable in addition to its recognition performance. This paper aims to deal with real-time traffic sign recognition, i.e., localizing what type of traffic sign appears in which area of an input image at a fast processing time. To achieve this goal, we first propose an extremely fast detection module, which is 20 times faster than the existing best detection module. Our detection module is based on traffic sign proposal extraction and classification built upon a color probability model and a color HOG. Then, we harvest from a convolutional neural network to further classify the detected signs into their subclasses within each superclass. Experimental results on both German and Chinese roads show that both our detection and classification methods achieve comparable performance with the state-of-the-art methods, with significantly improved computational efficiency.
KeywordTraffic Sign Detection Traffic Sign Recognition Real-time Color Probability Model
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TITS.2015.2482461
WOS KeywordRECOGNITION ; ALGORITHMS
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61375043 ; Education Bureau of Fujian Province, China(JK2011044) ; Xiamen Science and Technology Plan through the University Innovation Project(3502Z20131158) ; 61273290)
WOS Research AreaEngineering ; Transportation
WOS SubjectEngineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS IDWOS:000379908100021
Citation statistics
Cited Times:27[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12159
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.Xiamen Univ Technol, Xiamen 361024, Peoples R China
Recommended Citation
GB/T 7714
Yang, Yi,Luo, Hengliang,Xu, Huarong,et al. Towards Real-Time Traffic Sign Detection and Classification[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2016,17(7):2022-2031.
APA Yang, Yi,Luo, Hengliang,Xu, Huarong,&Wu, Fuchao.(2016).Towards Real-Time Traffic Sign Detection and Classification.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,17(7),2022-2031.
MLA Yang, Yi,et al."Towards Real-Time Traffic Sign Detection and Classification".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 17.7(2016):2022-2031.
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