基于D-S证据理论的特征融合车标识别方法

在针对现有的智能交通对车辆多维信息识别存在识别精度不高的情况，特别是对于车标识别，很大程度上识别结果依赖于高分辨和高质量的图像.提出了一种新的车标识别方法，用于识别卡口捕获的低质量车标图像，该方法是基于D-S证据理论的特征融合方法，提取Hu不变矩和HOG特征，采用不同的分类器构造基本概率分配（BPA），采用改进D-S证据理论进行融合，根据判别规则给出最终的识别结果.通过实验证明在低分辨的情况下仍能保持较高的准确率，分类准确率达94.29%，相比单一的特征识别，具有更强的鲁棒性.

Vehicle Logo Recognition Method of Feature Fusion Based on D-S Evidence Theory

For the intelligent traffic, there are inaccuracies in the multi-dimensional information identification of vehicles. Especially for vehicle logo recognition, the recognition results depend largely on high-resolution and high-quality images. A new vehicle logo identification method is proposed for distinguishing low-quality vehicle image captured at the bayonet. This method is based on the feature fusion of D-S evidence theory, extracts Hu invariant moments and HOG features, and uses different classifiers,the basic probability distribution (BPA) is constructed, the improved D-S evidence theory is used to fuse, and the final recognition result is given according to the discriminant rule. Through experiments, it is proved that the accuracy can be maintained at a low resolution, and the classification accuracy is 94.29%, which is more robust than a single feature recognition.