Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (21): 203-208.DOI: 10.3778/j.issn.1002-8331.2006-0320

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Adaptive Feature Fusion Network for Crowd Counting

ZUO Jianhao, JIANG Wengang   

  1. School of Electronic Information, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, China
  • Online:2021-11-01 Published:2021-11-04



  1. 江苏科技大学 电子信息学院,江苏 镇江 212003


In an attempt to solve the problems of scale change and multi-level feature fusion in population counting method, inspired by U-Net encoder decoder structure network, an adaptive feature fusion network is proposed to carry out accurate population counting. The Adaptive Feature Fusion Module(AFFM) is proposed to efficiently aggregate the high-level semantic information and low-level spatial detail extracted by the encoder branch according to the needs of decoder branch. The Adaptive Context Extractor(ACE) is proposed to extract multi-scale context information on multiple effective field-of-views, then these features are adaptively fused to improve the robustness of the network to scale changes. By conducting exhaustive experiments on Shanghai Tech, UCF-CC-50 and UCF-QNRF, the results show that the network has high accuracy and robustness.

Key words: crowd counting, Convolutional Neural Network(CNN), density estimation, multi?level features, scale variation, feature fusion



关键词: 人群计数, 卷积神经网络, 密度估计, 多层级特征, 尺度变化, 特征融合