Traffic sign recognition algorithm based on shape signature and dual-tree complex wavelet transform  

作　　者：蔡自兴 谷明琴 

机构地区：[1]School of Information Science and Engineering, Central South University

出　　处：《Journal of Central South University》2013年第2期433-439,共7页中南大学学报（英文版）

基　　金：Projects(90820302, 60805027) supported by the National Natural Science Foundation of China;Project(200805330005) supported by Research Fund for Doctoral Program of Higher Education, China;Project(2009FJ4030) supported by Academician Foundation of Hunan Province, China

摘　　要：A novel traffic sign recognition system is presented in this work. Firstly, the color segmentation and shape classifier based on signature feature of region are used to detect traffic signs in input video sequences. Secondly, traffic sign color-image is preprocessed with gray scaling, and normalized to 64×64 size. Then, image features could be obtained by four levels DT-CWT images. Thirdly, 2DICA and nearest neighbor classifier are united to recognize traffic signs. The whole recognition algorithm is implemented for classification of 50 categories of traffic signs and its recognition accuracy reaches 90%. Comparing image representation DT-CWT with the well-established image representation like template, Gabor, and 2DICA with feature selection techniques such as PCA, LPP, 2DPCA at the same time, the results show that combination method of DT-CWT and 2DICA is useful in traffic signs recognition. Experimental results indicate that the proposed algorithm is robust, effective and accurate.A novel traffic sign recognition system is presented in this work. Firstly, the color segmentation and shape classifier based on signature feature of region are used to detect traffic signs in input video sequences. Secondly, traffic sign color-image is preprocessed with gray scaling, and normalized to 64×64 size. Then, image features could be obtained by four levels DT-CWT images. Thirdly, 2DICA and nearest neighbor classifier are united to recognize traffic signs. The whole recognition algorithm is implemented for classification of 50 categories of traffic signs and its recognition accuracy reaches 90%. Comparing image representation DT-CWT with the well-established image representation like template, Gabor, and 2DICA with feature selection techniques such as PCA, LPP, 2DPCA at the same time, the results show that combination method of DT-CWT and 2DICA is useful in traffic signs recognition. Experimental results indicate that the proposed algorithm is robust, effective and accurate.

关 键 词：traffic sign recognition SIGNATURE DT-CWT 2DICA nearest neighbor classifier 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]