Overview of Crowd Counting

2020 ◽  
pp. 500-512
Author(s):  
Peizhi Zeng ◽  
Jun Tan
Keyword(s):  
Crowd Counting

scholarly journals Multi-Scale Context Aggregation Network with Attention-Guided for Crowd Counting

2020 ◽  
Author(s):  
Xin Wang ◽  
Rongrong Lv ◽  
Yang Zhao ◽  
Tangwen Yang ◽  
Qiuqi Ruan
Keyword(s):  
Crowd Counting ◽  
Multi Scale

PHNet: Parasite-Host Network for Video Crowd Counting

2021 ◽  
Author(s):  
Shiqiao Meng ◽  
Jiajie Li ◽  
Weiwei Guo ◽  
Lai Ye ◽  
Jinfeng Jiang
Keyword(s):  
Crowd Counting

scholarly journals Fine-Grained Crowd Counting

2021 ◽  
Vol 30 ◽  
pp. 2114-2126
Author(s):  
Jia Wan ◽  
Nikil Senthil Kumar ◽  
Antoni B. Chan
Keyword(s):  
Crowd Counting ◽  
Fine Grained

scholarly journals Convolutional Neural Network for Crowd Counting on Metro Platforms

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 703
Author(s):  
Jun Zhang ◽  
Jiaze Liu ◽  
Zhizhong Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Estimation Error ◽  
Image Features ◽  
Urban Rail Transit ◽  
Crowd Counting ◽  
Passenger Flow ◽  
Urban Rail ◽  
Density Map ◽  
Flow Detection

Owing to the increased use of urban rail transit, the flow of passengers on metro platforms tends to increase sharply during peak periods. Monitoring passenger flow in such areas is important for security-related reasons. In this paper, in order to solve the problem of metro platform passenger flow detection, we propose a CNN (convolutional neural network)-based network called the MP (metro platform)-CNN to accurately count people on metro platforms. The proposed method is composed of three major components: a group of convolutional neural networks is used on the front end to extract image features, a multiscale feature extraction module is used to enhance multiscale features, and transposed convolution is used for upsampling to generate a high-quality density map. Currently, existing crowd-counting datasets do not adequately cover all of the challenging situations considered in this study. Therefore, we collected images from surveillance videos of a metro platform to form a dataset containing 627 images, with 9243 annotated heads. The results of the extensive experiments showed that our method performed well on the self-built dataset and the estimation error was minimum. Moreover, the proposed method could compete with other methods on four standard crowd-counting datasets.

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