针对现有智能交通管理中车辆类型识别方法存在分类器效率较低等诸多问题,通过构造一种新的分类器,建立了一种智能交通车辆类型识别新方法.首先采用边缘梯度直方图进行图像特征提取,然后通过融合纠错编码技术和K-近邻分类器构造新分类器实现车辆类型的分类.通过大量实验仿真分析比对表明,该方法不仅能将多类分类问题转化成多个两分类问题,而且使车辆类型识别效率提高了2%,鲁棒性好.因此,该方法在"互联网+"智能交通管理中具有广阔的应用前景和推广价值.%Since the available vehicle type identification methods of the intelligent traffic management have various problems, such as low classifier efficiency,a new classifier was constructed to establish a vehicle type identification method for intelligent traffic. The edge gradient histogram is used to extract the image characteristics. The error correction coding technology and K-nearest neighbor classifier are fused to construct the new classifier to classify the vehicle types. The analysis and comparison results of a large number of experimental simulation show that the method can transform the multi-class classification problem into the multiple binary classification problem,make the vehicle type identification efficiency increased by 2%,and has the characteristic of good robustness. The method used in″Internet+″intelligent traffic management has broad application prospect and high promotion value.
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