梯度直方图卷积特征的胶囊网络在交通监控下的车型分类

为了充分利用图像信息以提高现有交通监控下车型分类的效果，在胶囊网络的基础上增加梯度直方图卷积（HOG-C）特征提取方法，提出HOG-C特征的胶囊网络模型——HOG-C CapsNet。首先，使用梯度统计特征提取层对图像中的梯度信息进行统计，构建方向梯度直方图（HOG）特征图；其次，使用卷积层提取出图像的颜色信息，把提取出的颜色信息与HOG特征图融合构成HOG-C特征图；最后，输入卷积层提取HOG-C特征图的抽象特征，并通过胶囊网络对提取的抽象特征进行具有三维空间特征表达的胶囊封装，使用动态路由算法实现车型分类。在BIT-Vehicle数据集上对该模型和其他相关模型进行的对比实验中，该模型得到98.17%的准确率、97.98%的平均精确率均值（MAP）、98.42%的平均召回率均值（MAR）和98.20%的综合评价指标。实验结果表明，该模型在交通监控下的车型分类上具有更好的效果。

Vehicle classification based on HOG-C CapsNet in traffic surveillance scenarios

To improve the performance of vehicle classification by making full use of image information from traffic surveillance, Histogram of Oriented Gradient Convolutional (HOG-C) features extraction method was added on the capsule network, and a Capsule Network model fusing with HOG-C features (HOG-C CapsNet) was proposed. Firstly, the gradient data in the images were calculated by the gradient statistical feature extraction layer, and then the Histogram of Oriented Gradient (HOG) feature map was plotted. Secondly, the color information of the image was extracted by the convolutional layer, and then the HOG-C feature map was plotted with the extracted color information and HOG feature map. Finally, the HOG feature map was input into to the convolutional layer extract its abstract features, and the abstract features were encapsulated through a capsule network into capsules with the three-dimensional spatial feature representation, so as to realize the vehicle classification by dynamic routing algorithm. Compared with other related models on the BIT-Vehicle dataset, the proposed model has the accuracy of 98.17%, the Mean Average Precision (MAP) of 97.98%, the Mean Average Recall (MAR) of 98.42% and the comprehensive evaluation index of 98.20%. Experimental results show that the vehicle classification in traffic surveillance scenarios can be achieved with better performance by using HOG-C CapsNet.