Authors
Li Wang, Fangbo Zhou, Huailin Zhao*
School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, China
*Corresponding author. Email: [email protected]
Corresponding Author
Huailin Zhao
Received 10 September 2019, Accepted 4 December 2020, Available Online 31 December 2020.
DOI
https://doi.org/10.2991/jrnal.k.201215.015How to use a DOI?
Keywords
Global reasoning unit; graph convolutional network; crowd density estimation
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.
Copyright
© 2020 The Authors. Published by ALife Robotics Corp. Ltd.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).