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	Salient Traffic Sign Recognition Based on Sparse Representation of Visual Perception
	Li, Ce1; Hu, Yaling1; Xiao, Limei1; Tian, Lihua2
	2012
关键词	Traffic sign detection Traffic sign recogntion Visual saliency Quaternion Fourier transform Sparse coding Support Vector Machine (SVM)
页码	273-278
英文摘要	This paper proposes a new approach to recognize salient traffic signs, which is based on sparse representation of visual perception via visual saliency and speeded up robust features (SURF) algorithm. The proposed algorithm deals with two tasks: traffic signs detection and traffic signs recognition. Firstly, multi-scale phase spectrum of quaternion Fourier transformation method is used to obtain the location of traffic signs in scenes image. Secondly, traffic signs local sparse features are extracted by the improved algorithm based on SURF descriptors and locality-constrained linear coding (LLC) method. Finally, linear support vector machine (SVM) is used to train classifier and test recognition accuracy rate of ban traffic signs. Extensive experiments on 1000 images show that our approach can improve recognition accuracy rate and reduce running time.
会议录出版者	IEEE
会议录出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
语种	英语
WOS研究方向	Computer Science ; Engineering ; Remote Sensing
WOS记录号	WOS:000318878700052
内容类型	会议论文
源URL	[http://119.78.100.223/handle/2XXMBERH/37262]
专题	新能源学院 电气工程与信息工程学院
作者单位	1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou, Peoples R China; 2.Xi An Jiao Tong Univ, Inst Artificial Intelligence & Robot, Xian, Peoples R China
推荐引用方式 GB/T 7714	Li, Ce,Hu, Yaling,Xiao, Limei,et al. Salient Traffic Sign Recognition Based on Sparse Representation of Visual Perception[C]. 见:.

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