Traffic sign recognition using HOG-SVM and grid search

Author

Chang Yao ; Feng Wu ; Hou-jin Chen ; Xiao-li Hao ; Yan Shen

Author_Institution

Sch. of Electron. & Inf. Eng., Beijing Jiaotong Univ., Beijing, China

fYear

2014

fDate

19-23 Oct. 2014

Firstpage

962

Lastpage

965

Abstract

Considering the lower accuracy of existing traffic sign recognition methods, a new traffic sign recognition method using histogram of oriented gradient - support vector machine (HOG-SVM) and grid search (GS) is proposed. First, the histogram of oriented gradient (HOG) is used to extract the characteristics of traffic sign. Then the grid search technique is applied to optimize the parameters of support vector machine (SVM). Finally, the traffic sign is recognized by using the trained SVM classifier. Experimental results indicate that the proposed method could achieve high accuracy for traffic sign recognition.

Keywords

image classification; search problems; support vector machines; traffic engineering computing; GS; HOG-SVM; SVM classifier; grid search technique; histogram of oriented gradient; support vector machine; traffic sign recognition; Data mining; Feature extraction; Image color analysis; Image recognition; Kernel; Support vector machines; Training; Grid search (GS); histogram of oriented gradient (HOG); support vector machine (SVM); traffic sign recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing (ICSP), 2014 12th International Conference on

Conference_Location

Hangzhou

ISSN

2164-5221

Print_ISBN

978-1-4799-2188-1

Type

conf

DOI

10.1109/ICOSP.2014.7015147

Filename

7015147