基于深度对抗式网络的人群计数方法

Crowd counting method based on the depth antagonism network

对人群密度大、遮挡严重以及分不均等因素造成人群计数困难的问题,本文提出了一种深度对抗式网络的人群计数模型,该模型主要分为生成器网络和判别器网络。首先利用具有良好的迁移能力和特征提取能力VG G16的前十层作为前端模块,以初步提取特征;然后,为应对人群遮挡严重以及分布不均的情况,使用我们设计的深度扩张卷积模块来聚合人群信息,并将浅层与深层人头特征进行融合,以增强网络对人群的适应能力。并在此过程中,使用扩张卷积代替传统的卷积层,在不损失图像分辨率的情况下对图像进行特征提取;最后,将密度图与标签密度图输入判别器网络进行判别,目的是生成与标签密度图更为相似的密度图,提高人群计数的准确性。实验结果表明,与其他方法相比,本文方法无论是在客观指标或者主管视觉方面,均具有较好的效果。

In this paper,we propose a population counting model of deep adversar y network,which is mainly divided into generator network and discriminator network.First,the first ten layers of VGG16with good migration ability and feature extraction ability are used as the front-end modules to initially extract features;then,in response to th e serious occlusion and uneven distribution of the crowd,the deep dilated convolution module designed by us is used to aggregate the crowd in formation,and the shallow and deep head features are fused to enhance the adaptability of the network to the crowd.In this process,th e expansion convolution is used instead of the traditional convolution layer to extract the features of the image without losing the resolu tion of the image.Finally,the density map and the label density map are input into the discriminator network to distinguish,so as to ge nerate the density map more similar to the label density map and improve the accuracy of population counting.The experimental results show t hat,compared with other methods,the method in this paper has better results in both objective indicators and supervisor vision.