Traffic signs recognition based on PCA-SIFT

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.

[1]  De Xu,et al.  Vision based starting position recognition and positioning control for thin steel sheet welding robot , 2013 .

[2]  Zhang Shu-xia.  Research on SIFT Algorithm of Feature Matching , 2010 .

[3]  Zhe Liu,et al.  Investigation on Traffic Signs Recognition Based on BP Neural Network and Invariant Moments , 2012 .

[4]  Wang Zhe-shen.  Road Traffic Sign Detection Using Color and Shape , 2007 .

[5]  Pietro Perona,et al.  A Bayesian hierarchical model for learning natural scene categories , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  David G. Lowe,et al.  Fitting Parameterized Three-Dimensional Models to Images , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Ping Kuang,et al.  Real-time road lane recognition using fuzzy reasoning for AGV vision system , 2004, 2004 International Conference on Communications, Circuits and Systems (IEEE Cat. No.04EX914).

[8]  Michael Isard,et al.  Bundling features for large scale partial-duplicate web image search , 2009, CVPR.

[9]  Shi Yue-xiang,et al.  Extraction of Image Semantic Attributes and Its Application , 2007 .

[10]  Bo Li,et al.  An Adaptive Algorithm for Robust Visual Codebook Generation and Its Natural Scene Categorization Application: An Adaptive Algorithm for Robust Visual Codebook Generation and Its Natural Scene Categorization Application , 2010 .

[11]  Xuan Wang,et al.  Design of a Ball-shaped Robot with Stereovision-based Localization , 2012 .