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Congested Crowd Counting via Adaptive Multi-Scale Context Learning †

In this paper, we propose a novel congested crowd counting network for crowd density estimation, i.e., the Adaptive Multi-scale Context Aggregation Network (MSCANet). MSCANet efficiently leverages the spatial context information to accomplish crowd density estimation in a complicated crowd scene. To...

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Autores principales: Zhang, Yani, Zhao, Huailin, Duan, Zuodong, Huang, Liangjun, Deng, Jiahao, Zhang, Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8198824/
https://www.ncbi.nlm.nih.gov/pubmed/34072408
http://dx.doi.org/10.3390/s21113777
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author Zhang, Yani
Zhao, Huailin
Duan, Zuodong
Huang, Liangjun
Deng, Jiahao
Zhang, Qing
author_facet Zhang, Yani
Zhao, Huailin
Duan, Zuodong
Huang, Liangjun
Deng, Jiahao
Zhang, Qing
author_sort Zhang, Yani
collection PubMed
description In this paper, we propose a novel congested crowd counting network for crowd density estimation, i.e., the Adaptive Multi-scale Context Aggregation Network (MSCANet). MSCANet efficiently leverages the spatial context information to accomplish crowd density estimation in a complicated crowd scene. To achieve this, a multi-scale context learning block, called the Multi-scale Context Aggregation module (MSCA), is proposed to first extract different scale information and then adaptively aggregate it to capture the full scale of the crowd. Employing multiple MSCAs in a cascaded manner, the MSCANet can deeply utilize the spatial context information and modulate preliminary features into more distinguishing and scale-sensitive features, which are finally applied to a 1 × 1 convolution operation to obtain the crowd density results. Extensive experiments on three challenging crowd counting benchmarks showed that our model yielded compelling performance against the other state-of-the-art methods. To thoroughly prove the generality of MSCANet, we extend our method to two relevant tasks: crowd localization and remote sensing object counting. The extension experiment results also confirmed the effectiveness of MSCANet.
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spelling pubmed-81988242021-06-14 Congested Crowd Counting via Adaptive Multi-Scale Context Learning † Zhang, Yani Zhao, Huailin Duan, Zuodong Huang, Liangjun Deng, Jiahao Zhang, Qing Sensors (Basel) Article In this paper, we propose a novel congested crowd counting network for crowd density estimation, i.e., the Adaptive Multi-scale Context Aggregation Network (MSCANet). MSCANet efficiently leverages the spatial context information to accomplish crowd density estimation in a complicated crowd scene. To achieve this, a multi-scale context learning block, called the Multi-scale Context Aggregation module (MSCA), is proposed to first extract different scale information and then adaptively aggregate it to capture the full scale of the crowd. Employing multiple MSCAs in a cascaded manner, the MSCANet can deeply utilize the spatial context information and modulate preliminary features into more distinguishing and scale-sensitive features, which are finally applied to a 1 × 1 convolution operation to obtain the crowd density results. Extensive experiments on three challenging crowd counting benchmarks showed that our model yielded compelling performance against the other state-of-the-art methods. To thoroughly prove the generality of MSCANet, we extend our method to two relevant tasks: crowd localization and remote sensing object counting. The extension experiment results also confirmed the effectiveness of MSCANet. MDPI 2021-05-29 /pmc/articles/PMC8198824/ /pubmed/34072408 http://dx.doi.org/10.3390/s21113777 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Yani
Zhao, Huailin
Duan, Zuodong
Huang, Liangjun
Deng, Jiahao
Zhang, Qing
Congested Crowd Counting via Adaptive Multi-Scale Context Learning †
title Congested Crowd Counting via Adaptive Multi-Scale Context Learning †
title_full Congested Crowd Counting via Adaptive Multi-Scale Context Learning †
title_fullStr Congested Crowd Counting via Adaptive Multi-Scale Context Learning †
title_full_unstemmed Congested Crowd Counting via Adaptive Multi-Scale Context Learning †
title_short Congested Crowd Counting via Adaptive Multi-Scale Context Learning †
title_sort congested crowd counting via adaptive multi-scale context learning †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8198824/
https://www.ncbi.nlm.nih.gov/pubmed/34072408
http://dx.doi.org/10.3390/s21113777
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