车标自动识别是智能交通系统中机动车辆信息采集的关键内容。根据车标具有丰富边缘信息的特征, 文章应用HOG (梯度方向直方图)的特征值, 采用SVM(支持向量机)的分类工具实现了车标的快速检测与识别。并提出一种改进的HOG特征值, 在车标检测识别准确率上取得了显著的效果。大量实验数据以及在智能交通系统中的应用表明, 该方法具有较强的鲁棒性和实用价值。

Automatic logo recognition is one of the key elements of vehicle information collection in an intelligent transportation system (ITS). Considering the abundant marginal information about the vehicle logo, this paper applies the characteristic values of the Histograms of Oriented Gradients (HOG) and implements the fast vehicle logo detection and recognition with the help of Support Vector Machine (SVM). Furthermore, it proposes an improved HOG, which achieves evident results in terms of the accuracy of vehicle logo detection and recognition. Large amounts of experimental data and applications in ITS show that this method is fairly robust and practically useful.