关键词: 道路交通标志识别 模式分类 分类器 支持向量机 BP算法

Abstract:

Support vector machine（SVM）is a novel machine learning method based on the statistical learning theory,which can avoid over-learning and provides go  od generalization performance.In this research,Multi-category SVM（M-SVM）is applied to traffic sign recognition and is compared with the BP algorithm,w   which has been commonly used in neural networks.116 Chinese ideal signs and 23 Japanese signs are first chosen for training M-SVMs and BP intelligent cl  assifiers.Next,noise signs,level twisted signs from real Chinese and Japanese traffic signs are selected as a test set for the purpose of two network te sting.Experimental results indicate that SVM has achieved a nearly 100% recognition rate and has certain advantages over the BP algorithm in approximate d classification for traffic signs.In fine classification,SVM shows its superiority to the BP algorithm.Based on the analysis of the results,one may com e to a conclusion that SVM algorithm is well worth the research efforts and is very promising in the area of trafficsign recognition.

Key words: （traffic sign recognition,pattern classification,classifier,support vector machine,BP algorithm）