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A survey of crowd counting and density estimation based on convolutional neural network

  • East China Jiaotong University
  • Harbin Institute of Technology Shenzhen
  • Peng Cheng Laboratory
  • University of Leicester

Research output: Contribution to journalArticlepeer-review

Abstract

Crowd counting and crowd density estimation methods are of great significance in the field of public security. Estimating crowd density and counting from single image or video frame has become an essential part of a computer vision system in various scenarios. In this paper, we comprehensively review the recent research advancement on crowd counting and density estimation. First of all, we introduce the background of crowd counting and crowd density estimation. Second, the traditional crowd counting methods are summarized. Third, we focus on reviewing the crowd counting and crowd density methods based on convolutional neural network (CNN) models. Next, we report and discuss the experimental results of a number of typical methods on benchmark datasets. Finally, we present the promising future directions of crowd counting and crowd density.

Original languageEnglish
Pages (from-to)224-251
Number of pages28
JournalNeurocomputing
Volume472
DOIs
StatePublished - 1 Feb 2022
Externally publishedYes

Keywords

  • Convolutional neural network
  • Crowd counting
  • Crowd density estimation
  • Deep learning

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