关键词: 交通标志识别（TSR）, 方向梯度直方图（HOG）, 局部二值模式（LBP）, 颜色特征, 特征融合, 支持向量机（SVM）

Abstract: Taking recognition rate, time complexity and robustness all into consideration, this paper proposes a traffic sign recognition algorithm based on edge, texture and color feature fusion and Support Vector Machine（SVM）. It conducts the statistical average on extracted Histogram of Oriented Gradient（HOG） features which can describe the edge information of traffic signs images. The reduced-dimension HOG-maxLBP features are obtained by fusing with Local Binary Pattern（LBP） features that can represent traffic sign internal texture information. The color features are concatenated with HOG-maxLBP features as the final features. Traffic sign training and classification are performed using SVM. Experimental results show that the proposed algorithm not only improves the traffic sign recognition rate, but also reduces the time complexity and enhances the system robustness.

Key words: Traffic Sign Recognition（TSR）, Histogram of Oriented Gradient（HOG）, Local Binary Pattern（LBP）, color features, feature fusion, Support Vector Machine（SVM）