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Neural Computing and Applications

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13.10.2018 | Original Article

Single-column CNN for crowd counting with pixel-wise attention mechanism

verfasst von: Bisheng Wang, Guo Cao, Yanfeng Shang, Licun Zhou, Youqiang Zhang, Xuesong Li

Erschienen in: Neural Computing and Applications | Ausgabe 7/2020

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Abstract

This paper presents a novel method for accurate people counting in highly dense crowd images. The proposed method consists of three modules: extracting foreground regions (EF), pixel-wise attention mechanism (PAM) and single-column density map estimator (S-DME). EF can suppress the disturbance of complex background efficiently with a fully convolutional network, PAM performs pixel-wise classification of crowd images to generate high-quality local crowd density maps, and S-DME is a carefully designed single-column network that can learn more representative features with much fewer parameters. In addition, two new evaluation metrics are introduced to get a comprehensive understanding of the performance of different modules in our algorithm. Experiments demonstrate that our approach can get the state-of-the-art results on several challenging datasets including our dataset with highly cluttered environments and various camera perspectives.

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Titel: Single-column CNN for crowd counting with pixel-wise attention mechanism
verfasst von: Bisheng Wang
Guo Cao
Yanfeng Shang
Licun Zhou
Youqiang Zhang
Xuesong Li
Publikationsdatum: 13.10.2018
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 7/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3810-9

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