The Multi-channel and Multi-scale Network for Crowd Counting

Abstract

In this paper, we propose a novel multi-channel and multi-scale network for processing crowds in crowded scenarios and improving counting accuracy. In the estimated crowd count study, different distribution groups have different contributions to the total number of crowd, and the more crowded people have stricter requirements on details. Therefore, we designed two branches in the crowd counting network: the backbone network performs feature extraction operations on the original image, which mainly obtains effective information from the global, and our branch network focuses on the crowd gathering area, which better focuses on the details of the crowd distribution. Finally, the global information is complemented with local details to obtain high-quality feature expressions. To deal with scale changes, Inspired by atrous spatial pyramid pooling structures, we introduce dilated convolution with different sampling rates in the network to expand the receptive field. We carried out a large number of experimental verifications on popular data sets, and the proposed method is superior to existing methods.