A novel traffic sign recognition algorithm based on sparse representation and dictionary learning | |
Wang, Bin1; Kong, Bin1; Ding, Dawen2; Wang, Can1; Yang, Jing1![]() | |
刊名 | JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
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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 |
DOI | 10.3233/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) |
内容类型 | 期刊论文 |
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. |
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