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A novel traffic sign recognition algorithm based on sparse representation and dictionary learning

文献类型：期刊论文

作者Wang, Bin1; Kong, Bin1; Ding, Dawen2; Wang, Can1; Yang, Jing1
刊名JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
出版日期2017
卷号32期号:5页码:3775-3784
关键词Compressive Sensing Sparse Representation Traffic Sign Recognition Over-complete Dictionary Learning
DOI10.3233/JIFS-169310
文献子类Article
英文摘要In this paper, we have proposed a novel traffic sign recognition algorithm based on sparse representation and dictionary learning. In the past period of research and applications of traffic sign recognition, most of the traffic sign recognition algorithms are based on statistical learning, neural networks and template matching algorithm. In these algorithms, they need high-dimensional mapping during classification, resulting in huge amount of calculation. Meanwhile, when the external environment changes, such as illumination, deformation and occlusion, the recognition rate will be further reduced. The proposed sparse representation theory has much better performance to solve the problems of external environment changed and while we use dictionary learning method to build a traffic sign over-complete redundant dictionary, the experimental results clearly showed that the algorithm we proposed has much better performance than traditional algorithm and also has much higher recognition rates.
WOS关键词SUPPORT VECTOR MACHINES ; TUTORIAL
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000400023600044
资助机构Major Research Plan of the National Natural Science Foundation of China(91120307 ; Major Research Plan of the National Natural Science Foundation of China(91120307 ; Major Research Plan of the National Natural Science Foundation of China(91120307 ; Major Research Plan of the National Natural Science Foundation of China(91120307 ; Major Research Plan of the National Natural Science Foundation of China(91120307 ; Major Research Plan of the National Natural Science Foundation of China(91120307 ; Major Research Plan of the National Natural Science Foundation of China(91120307 ; Major Research Plan of the National Natural Science Foundation of China(91120307 ; 61304122 ; 61304122 ; 61304122 ; 61304122 ; 61304122 ; 61304122 ; 61304122 ; 61304122 ; 61105112) ; 61105112) ; 61105112) ; 61105112) ; 61105112) ; 61105112) ; 61105112) ; 61105112) ; Major Research Plan of the National Natural Science Foundation of China(91120307 ; Major Research Plan of the National Natural Science Foundation of China(91120307 ; Major Research Plan of the National Natural Science Foundation of China(91120307 ; Major Research Plan of the National Natural Science Foundation of China(91120307 ; Major Research Plan of the National Natural Science Foundation of China(91120307 ; Major Research Plan of the National Natural Science Foundation of China(91120307 ; Major Research Plan of the National Natural Science Foundation of China(91120307 ; Major Research Plan of the National Natural Science Foundation of China(91120307 ; 61304122 ; 61304122 ; 61304122 ; 61304122 ; 61304122 ; 61304122 ; 61304122 ; 61304122 ; 61105112) ; 61105112) ; 61105112) ; 61105112) ; 61105112) ; 61105112) ; 61105112) ; 61105112)
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/33503]  
专题合肥物质科学研究院_中科院合肥智能机械研究所
作者单位1.Chinese Acad Sci, Inst Intelligent Machine, Ctr Biomimet Sensing & Control Res, Hefei, Anhui, Peoples R China
2.Univ Sci & Technol China, West Campus, Hefei 230027, Anhui, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Bin,Kong, Bin,Ding, Dawen,et al. A novel traffic sign recognition algorithm based on sparse representation and dictionary learning[J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,2017,32(5):3775-3784.
APA Wang, Bin,Kong, Bin,Ding, Dawen,Wang, Can,&Yang, Jing.(2017).A novel traffic sign recognition algorithm based on sparse representation and dictionary learning.JOURNAL OF INTELLIGENT & FUZZY SYSTEMS,32(5),3775-3784.
MLA Wang, Bin,et al."A novel traffic sign recognition algorithm based on sparse representation and dictionary learning".JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 32.5(2017):3775-3784.

入库方式： OAI收割

来源：合肥物质科学研究院

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