Crowd Density Estimation based on Global Reasoning

Abstract

The problem of crowd counting in single images and videos has attracted more and more attention in recent years. The crowd counting task has made massive progress by now due to the Convolutional Neural Network (CNN). However, filters in the shallow convolutional layer of the CNN only model the local region rather than the global region, which cannot capture context information from the crowd scene efficiently. In this paper, we propose a Graph-based Global Reasoning (GGR) network for crowd counting to solve this problem. Each input image is processed by the VGG-16 network for feature extracting, and then the GGR Unit reasons the context information from the extracted feature. Especially, the extracted feature firstly is transformed from the feature space to the interaction space for global context reasoning with the Graph Convolutional Network (GCN). Then, the output of the GCN projects the context information from the interaction space to the feature space. The experiments on the UCF-QNRF dataset demonstrate the effectiveness of the proposed method.