Traffic signs recognition based on PCA-SIFT

Author

Gao Hongwei ; Liu Chuanyin ; Yu Yang ; Li Bin

Author_Institution

State Key Lab. of Robot., Shenyang Inst. of Autom., Shenyang, China

fYear

2014

Firstpage

5070

Lastpage

5076

Abstract

Traffic signs automatic recognition was researched in this paper. Traffic signs image preprocessing methods was introduced firstly. Secondly, feature extraction algorithm of traffic signs based on SIFT was elaborated, then a fast SIFT algorithm based on PCA dimensionality reduction was presented to extract the characteristics of traffic signs. Finally, the SVM classifier was studied. A large number of experimental results were completed to demonstrate the effectiveness and practicality of related algorithms.

Keywords

feature extraction; image classification; principal component analysis; support vector machines; traffic engineering computing; PCA dimensionality reduction; PCA-SIFT; SVM classifier; feature extraction algorithm; traffic signs automatic recognition; traffic signs image preprocessing methods; Classification algorithms; Educational institutions; Feature extraction; Histograms; Principal component analysis; Support vector machines; Vectors; PCA-SIFT; Recognition; SIFT; Traffic signs;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation (WCICA), 2014 11th World Congress on

Type

conf

DOI

10.1109/WCICA.2014.7053576

Filename

7053576

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3573572