文献类型：期刊文献

英文题名：A new traffic signs classification approach based on local and global features extraction

作者：He, Xiaojun[1]; Dai, Benqi[1]

机构：[1] School of Information and Control Engineering, East China University of Science and Technology, Shanghai, China

年份：2016

起止页码：121

外文期刊名：Proceedings of the 6th International Conference on Information Communication and Management, ICICM 2016

收录：EI(收录号：20170503293547)

语种：英文

外文关键词：Classification (of information) - Intelligent vehicle highway systems - Local binary pattern - Extraction - Intelligent systems - Signal reconstruction - Traffic signs - Support vector machines

摘要：In recent years, the automatic traffic signs recognition(TSR) has attracted researches' attention and it becomes a great challenge in Intelligent Transport System (ITS). Because of complex environmental or weather reasons, it is difficult for TSR to achieve high correct recognition rate and meet real-time at the same time. This paper focus on the traffic signs feature extraction and proposes a new effective approach to recognize the traffic signs with combining local feature and global feature. Center Symmetry Local Binary Pattern (CSLBP) is the improvement of Local Binary Pattern(LBP), We modify the CSLBP with multi sampling as the local feature. The global feature is the low frequency coefficients of Discrete Wavelet Transform (DWT) which owns good multi resolution ability. After extracted the two kinds of feature, we cascade them as the new feature to represent the traffic signs image. The final feature as the input data to classify the traffic signs with Support Vector Machine (SVM). In the GTSRB database, the result shows that the proposed approach achieves a accuracy of 97.67% which is superior to single feature approach and greatly reduces the recognition time. ? 2016 IEEE.