Multi-scale Supervised Attentive Encoder-Decoder Network for Crowd Counting

2020 ◽  
Vol 16 (1s) ◽  
pp. 1-20
Author(s):  
Anran Zhang ◽  
Xiaolong Jiang ◽  
Baochang Zhang ◽  
Xianbin Cao
Keyword(s):  
Crowd Counting ◽  
Multi Scale

scholarly journals Hybrid Graph Neural Networks for Crowd Counting

2020 ◽  
Vol 34 (07) ◽  
pp. 11693-11700 ◽  
Author(s):  
Ao Luo ◽  
Fan Yang ◽  
Xin Li ◽  
Dong Nie ◽  
Zhicheng Jiao ◽  
...  
Keyword(s):  
Network Architecture ◽  
Message Passing ◽  
Large Scale ◽  
State Of The Art ◽  
Density Variation ◽  
Feature Maps ◽  
Crowd Counting ◽  
Multi Scale ◽  
Crowd Density ◽  
Graph Neural Networks

Crowd counting is an important yet challenging task due to the large scale and density variation. Recent investigations have shown that distilling rich relations among multi-scale features and exploiting useful information from the auxiliary task, i.e., localization, are vital for this task. Nevertheless, how to comprehensively leverage these relations within a unified network architecture is still a challenging problem. In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph. Specifically, HyGnn integrates a hybrid graph to jointly represent the task-specific feature maps of different scales as nodes, and two types of relations as edges: (i) multi-scale relations capturing the feature dependencies across scales and (ii) mutual beneficial relations building bridges for the cooperation between counting and localization. Thus, through message passing, HyGnn can capture and distill richer relations between nodes to obtain more powerful representations, providing robust and accurate results. Our HyGnn performs significantly well on four challenging datasets: ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF_QNRF, outperforming the state-of-the-art algorithms by a large margin.

GCMNet: Gated Cascade Multi-scale Network for Crowd Counting

2021 ◽  
pp. 403-417
Author(s):  
Jinfang Zheng ◽  
Panpan Zhao ◽  
Jinyang Xie ◽  
Chen Lyu ◽  
Lei Lyu
Keyword(s):  
Crowd Counting ◽  
Multi Scale ◽  
Scale Network

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

2020 ◽  
Vol 1650 ◽  
pp. 032070
Author(s):  
Pengze Wang ◽  
Wei Wu ◽  
Yang Su ◽  
Xin Li ◽  
Yongsheng Duan
Keyword(s):  
Crowd Counting ◽  
Multi Scale ◽  
Scale Network

A multi-scale and multi-level feature aggregation network for crowd counting

2021 ◽  
Vol 423 ◽  
pp. 46-56
Author(s):  
Fushun Zhu ◽  
Hua Yan ◽  
Xinyue Chen ◽  
Tong Li ◽  
Zhengyu Zhang
Keyword(s):  
Crowd Counting ◽  
Multi Scale ◽  
Feature Aggregation ◽  
Multi Level

Crowd counting by using multi-level density-based spatial information: A Multi-scale CNN framework

2020 ◽  
Vol 528 ◽  
pp. 79-91 ◽  
Author(s):  
Li Dong ◽  
Haijun Zhang ◽  
Yuzhu Ji ◽  
Yuxin Ding
Keyword(s):  
Spatial Information ◽  
Level Density ◽  
Crowd Counting ◽  
Multi Scale ◽  
Multi Level
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