江汉大学学报(自然科学版) ›› 2016, Vol. 44 ›› Issue (2): 119-125.doi: 10.16389/j.cnki.cn42-1737/n.2016.02.004

• 计算机科学与应用 • 上一篇    下一篇

基于HSV空间和形状特征的交通标志检测识别研究

陈亦欣1,叶锋1,肖锋1,李庆楠2   

  1. 1. 江汉大学 数学与计算机科学学院,湖北 武汉 430056;2. 武汉大学 计算机学院,湖北 武汉 430072
  • 出版日期:2016-04-28 发布日期:2016-05-05
  • 作者简介:陈亦欣(1985—),男,助教,博士,研究方向:数字版权管理。

Detection and Recognition of Traffic Signs Based on HSV Vision Model and Shape Features

CHEN Yixin1,YE Feng1,XIAO Feng1,LI Qingnan2   

  1. 1. School of Mathematics and Computer Science,Jianghan University,Wuhan 430056,Hubei,China;2. School of Computer,Wuhan University,Wuhan 430072,Hubei,China
  • Online:2016-04-28 Published:2016-05-05

摘要: 基于交通标志的颜色和几何形状特征,采用MATLAB 工具设计了一种交通标志检测识别方法。该方法由基于HSV 空间的颜色抽取、基于仿射变换的几何形状校正及判定、基于Gabor 滤波的特征向量提取和基于SVM的分类识别4部分组成。仿真结果表明,该方法减小了投影失真,有效实现了交通标志的检测与分类,且准确率较高。

关键词: 交通标志检测, HSV颜色空间, 仿射变换, Gabor滤波, SVM

Abstract: A detection and recognition method of traffic signs is implemented with MATLAB based on traffic signs′ color and shape features. This method consists of four parts which are image segmentation based on HSV color space,detection and affine transformation correction based on geometry features,extraction of eigenvector with Gabor filter,classification and recognition with support vector machine (SVM). Experimental results show that the proposed method can reduce projecting distortion,accurately classify and identify traffic signs.

Key words: traffic signs detection, HSV color space, affine transformation, Gabor filter, support vector machine(SVM)

中图分类号: