Title :
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
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;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2014 11th World Congress on
DOI :
10.1109/WCICA.2014.7053576
