A Global-Local Dual Branch Network for Congested Crowd Counting

被引：0
|
作者：
Di H. [1 ]
Song L. [1 ]
Yu X. [1 ]
Wang W. [1 ]
机构：
[1] School of Computer Science and Technology, Beijing Institute of Technology, Beijing
来源：
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2022年 / 42卷 / 11期
关键词：
crowd density estimation; deep learning; multi-scale learning; visual attention;
D O I：
10.15918/j.tbit1001-0645.2021.311
中图分类号：
学科分类号：
摘要：
Convolutional neural network based crowd counting methods have promoted a significant improvement in the accuracy of crowd counting. However, for congested crowd, huge scale variations of crowd heads and complex scenes still hinder the accuracy of crowd counting. In order to overcome this problem a global-local dual branch network was proposed. The local branch was arranged with the proposed scale-aware feature extraction modules to model the scale changes of the heads in congested crowds. The global branch was arranged with a localization-aware attention module to enhance the network's ability to discriminate between the crowd and the background objects. Then the extracted local features and global features were sent to the feature fusion branch to produce a crowd density map. The proposed method was evaluated on three commonly-used crowd counting datasets and one remote sensing object counting dataset. The quantitative and qualitative results show the effectiveness of the proposed method. © 2022 Beijing Institute of Technology. All rights reserved.
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页码：1175 / 1183
页数：8
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