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

  • Bisheng Wang
  • Guo Cao
  • Yanfeng Shang
  • Licun Zhou
  • Youqiang Zhang
  • Xuesong Li
Original Article
  • 39 Downloads

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.

Keywords

Crowd counting CNN Pixel-wise attention mechanism FCN 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Nanjing University of Science and TechnologyNan Jing CityChina
  2. 2.The Third Research Institute of the Ministry of Public SecurityShanghaiChina

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