基于多特征融合的交通标志分类

doi:10.6040/j.issn.1672-3961.0.2016.082

摘要/Abstract

摘要： 为有效提高交通标志分类的准确度,提出一种融合全局特征和局部特征的多特征交通标志分类方法。首先提取能够描述标志图像内部纹理信息的局部二值模式(local binary pattern, LBP)特征,再提取能够表示标志图像形状信息的方向梯度直方图(histogram of oriented gradient, HOG)特征和描述图像粗略轮廓信息的全局Gist特征,然后采用线性组合方式,实现特征融合互补,并通过主成分分析(principal components analysis, PCA)法进行数据降维,最后采用支持向量机(support vector machine, SVM)分类器进行交通标志训练与识别。试验结果表明:相对于单一特征的交通标志分类方法,基于多特征融合的算法获得了更高的分类精确度,同时也满足实时性要求。

关键词: 交通标志分类, 局部二值模式, 方向梯度直方图特征, Gist特征, 特征融合

Abstract: In order to effectively improve the accuracy of the traffic sign classification, a new method was proposed through fusing the global and local features. First, local binary pattern(LBP)feature was extracted which could describe the internal texture information of traffic sign image, and then histogram of oriented gradient(HOG)feature which could represent shape information and global gist feature with description of the rough outline of the image information were extracted, and then linear combination was used to achieve feature complementary. The principal component analysis(PCA)was used for data dimensionality reduction. Final traffic sign training and classification was carried out using support vector machine(SVM)classifier. The experiments showed that with respect to a single feature extraction classification of traffic signs, the algorithm based on multi-featured fusion achieveed higher classification accuracy, but also met real-time requirements.

Key words: Gist feature, feature fusion, local binary pattern, traffic sign classification, HOG feature

中图分类号:

TP391.4

王斌,常发亮,刘春生. 基于多特征融合的交通标志分类[J]. 山东大学学报(工学版), 2016, 46(4): 34-40.

WANG Bin, CHANG Faliang, LIU Chunsheng. Traffic sign classification based on multi-feature fusion[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(4): 34-40.

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