%0 Journal Article
%T 逐级细化的交通标志识别算法<br>Coarse-to-Fine Algorithm for Traffic Sign Recognition
%A 徐丹
%A 张绛丽
%A 于化龙
%A 左欣
%A 高尚
%J 数据采集与处理
%D 2018
%R 10.16337/j.1004-9037.2018.03.019
%X 针对交通标志识别中存在的识别精度和实时应用之间的矛盾，根据中国交通标志的特点，提出一种逐级细化的交通标志识别算法。首先进行粗分类，构建颜色属性-梯度直方图（Color name-histogram of gradient，CN-HOG）描述子表示每类标志的形状和颜色特征，采用线性支持向量机（Support vector machine，SVM）将交通标分为禁令标志、警告标志、指示标志、解除禁令标志和其他标志5大类；然后进行细分类，采用词袋模型中颜色和形状特征早融合的方式将颜色属性（Color name，CN）和尺度不变特征变换（Scale-invariant feature transform，SIFT）描述子相结合、利用高斯核SVM得到交通标志区域的最终类别标记。在公开数据集上的实验表明本文算法在满足实时应用的同时取得了99.15%的识别精度。<br>In this paper, a coarse-to-fine traffic sign recognition algorithm is proposed to alleviate the conflict between recognition precision and time consumption. In the coarse classification, a traffic sign region is represented with color name-histogram of gradient (CN-HOG) descriptors to describe its color and shape features. A linear support vector machine (SVM) classifier is used to classify the region into different categories:prohibitory, warning, mandatory, release of prohibitory and others. In the fine classification, the different fusion methods of color and shape features in Bag of Words model are discussed and the color-shape early fusion method is employed to combine the CN and scale-invariant feature transform (SIFT) descriptors. The final class labels of the region are obtained by Gaussian kernel SVM classifier. Experiments in public dataset show that the proposed algorithm satisfies real-time practice and meanwhile achieves a high classification precision of 99.15%.
%K 交通标志识别
%K 逐级细化
%K 词袋模型
%K 颜色属性-梯度直方图&lt
%K br&gt
%K traffic sign recognition
%K coarse-to-fine
%K bag of words
%K color name-histogram of gradient (CN-HOG)
%U http://sjcj.nuaa.edu.cn/ch/reader/view_abstract.aspx?file_no=20180319&flag=1