Traffic Sign Recognition Based on Extreme Learning Machine

被引：0

作者：

Xu, Yan ^{[1
]}

Wang, Quanwei ^{[1
]}

Wei, Zhenyu ^{[1
]}

机构：

[1] Tianjin Univ, Sch Elect Informat Engn, Tianjin 300072, Peoples R China

来源：

ELECTRICAL AND CONTROL ENGINEERING & MATERIALS SCIENCE AND MANUFACTURING | 2016年

基金：

中国国家自然科学基金;

关键词：

Traffic Sign Recognition; HOG; PCA; Extreme Learning Machine;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

As one of the important research directions of Intelligent Transportation System (ITS), Traffic Sign Recognition (TSR) has become a hot topic worldwide. Due to the wide variety of traffic signs and complexity of road traffic environment, efficiency-and correct recognition rate have to he taken into consideration in the design of TSR schemes. This paper uses principal component analysis (PCA) and extreme teaming machine (ELM) to recognize traffic signs. Firstly histograms of the oriented gradient (HOG) features of each traffic sign are extracted, after dimension reduction by PCA, the dimension-reduced PCA features are put into ELM to train for an optimized recognition model. Database used in the experiments is German Traffic Sign Detection Benchmark (GTSDB). Experimental results show that the proposed method is able to recognize traffic sign in real-time with a high correct recognition rate.

引用

页码：393 / 403

页数：11

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