CLC number: TP391.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2020-06-11
Cited: 0
Clicked: 5941
Citations: Bibtex RefMan EndNote GB/T7714
Jie-hao Huang, Xiao-guang Di, Jun-de Wu, Ai-yue Chen. A novel convolutional neural network method for crowd counting[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(8): 1150-1160.
@article{title="A novel convolutional neural network method for crowd counting",
author="Jie-hao Huang, Xiao-guang Di, Jun-de Wu, Ai-yue Chen",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="8",
pages="1150-1160",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900282"
}
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%A Jun-de Wu
%A Ai-yue Chen
%J Frontiers of Information Technology & Electronic Engineering
%V 21
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900282
TY - JOUR
T1 - A novel convolutional neural network method for crowd counting
A1 - Jie-hao Huang
A1 - Xiao-guang Di
A1 - Jun-de Wu
A1 - Ai-yue Chen
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
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SP - 1150
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%@ 2095-9184
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1900282
Abstract: Crowd density estimation, in general, is a challenging task due to the large variation of head sizes in the crowds. Existing methods always use a multi-column convolutional neural network (MCNN) to adapt to this variation, which results in an average effect in areas with different densities and brings a lot of noise to the density map. To address this problem, we propose a new method called the segmentation-aware prior network (SAPNet), which generates a high-quality density map without noise based on a coarse head-segmentation map. SAPNet is composed of two networks, i.e., a foreground-segmentation convolutional neural network (FS-CNN) as the front end and a crowd-regression convolutional neural network (CR-CNN) as the back end. With only the single dot annotation, we generate the ground truth of segmentation masks in heads. Then, based on the ground truth, FS-CNN outputs a coarse head-segmentation map, which helps eliminate the noise in regions without people in the density map. By inputting the head-segmentation map generated by the front end, CR-CNN performs accurate crowd counting estimation and generates a high-quality density map. We demonstrate SAPNet on four datasets (i.e., ShanghaiTech, UCF-CC-50, WorldExpo’10, and UCSD), and show the state-of-the-art performances on ShanghaiTech part B and UCF-CC-50 datasets.
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