为了避免景深和遮挡的干扰, 提高人群计数的准确性, 采用了LeNet-5,AlexNet和VGG-16 3种模型, 提取图像中不同景深目标的特性, 调整上述模型的卷积核尺寸和网络结构, 并进行了模型融合。构造出一种基于多模型融合的深度卷积神经网络结构, 网络最后两层采用卷积核大小为1×1的卷积层取代传统的全连接层, 对提取的特征图进行信息整合并输出密度图, 极大地降低了网络参量且取得了一定提升的数据, 兼顾了算法效率和精度, 进行了理论分析和实验验证。结果表明, 在公开人群计数数据集shanghaitech两个子集和UCF_CC_50子集上, 本文中计数方法的平均绝对误差和均方误差分别是97.99和158.02,23.36和41.86,354.27和491.68, 取得比现有传统人群计数方法更好的性能; 通过迁移实验证明所提出的人群计数模型具有良好的泛化能力。该研究对人群计数精度的提高是有帮助的。

To avoid the interference of depth of field and occlusion and improve the accuracy of crowd counting, three models of LeNet-5, AlexNet and VGG-16 were adopted and the characteristics of objects with different depth of field in the image were extracted. After adjusting the convolution core size and network structure of the above model, model fusion was carried out. A deep convolution neural network structure based on multi-model fusion was constructed. In the last two layers of the network, the convolution layer with convolution core size of 1×1 was used to replace the traditional full connection layer. The extracted feature maps were integrated with information and the density maps were output. The network parameters were greatly reduced and some improved data was obtained. The efficiency and accuracy of the algorithm were taken into account. The theoretical analysis and experimental verification were carried out. The results show that, in public population counting data set of two subsets of shanghaitech and UCF_CC_50, the mean absolute error and mean square error of this method are 97.99 and 158.02, 23.36 and 41.86, 354.27 and 491.68, respectively. It achieves better performance than the existing traditional crowd counting methods. At the same time, migration experiments are carried out. It proves that the population counting model proposed in this paper has good generalization ability. This study is helpful to improve the accuracy of population counting.