PDANet: Pyramid density-aware attention based network for accurate crowd counting

Saeed Amirgholipour, Wenjing Jia, Lei Liu, Xiaochen Fan, Dadong Wang, Xiangjian He

Research output: Journal PublicationArticlepeer-review

2 Citations (Scopus)

Abstract

Crowd counting, i.e., estimating the number of people in crowded areas, has attracted much interest in the research community. Although many attempts have been reported, crowd counting remains an open real-world problem due to the vast density variations and severe occlusion within the interested crowd area. In this paper, we propose a novel Pyramid Density-Aware Attention based network, abbreviated as PDANet, which leverages the attention, pyramid scale feature, and two branch decoder modules for density-aware crowd counting. The PDANet utilizes these modules to extract features of different scales while focusing on the relevant information and suppressing the misleading information. We also address the variation of crowdedness levels among different images with a Density-Aware Decoder (DAD) modules. For this purpose, a classifier is constructed to evaluate the density level of the input features and then passes them to the corresponding high and low density DAD modules. Finally, we generate an overall density map by considering the summation of low and high crowdedness density maps. Meanwhile, we employ different losses aiming to achieve a precise density map for the input scene. Extensive evaluations conducted on the challenging benchmark datasets well demonstrate the superior performance of the proposed PDANet in terms of the accuracy of counting and generated density maps over the well-known state-of-the-art approaches.

Original languageEnglish
Pages (from-to)215-230
Number of pages16
JournalNeurocomputing
Volume451
DOIs
Publication statusPublished - 3 Sep 2021
Externally publishedYes

Keywords

  • Attention module
  • Classification module
  • Convolutional neural networks
  • Crowd counting
  • Density ware
  • Pyramid module

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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