Laser & Optoelectronics Progress, Volume. 59, Issue 18, 1815004(2022)

Convolutional Neural Network Method for Crowd Counting Improved using Involution Operator

Zhaoxin Li, Shuhua Lu*, Lingqiang Lan, and Qiyuan Liu
Author Affiliations
  • College of Information and Cyber Security, People’s Public Security University of China, Beijing 102600, China
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    Figures & Tables(7)
    Crowd counting network structure
    Generation and working principle of Involution kernel
    Examples of density map generation
    Example of the ablation experiment
    • Table 1. Value of coefficient α in different datasets

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      Table 1. Value of coefficient α in different datasets

      Datasetα
      SHHA1000
      SHHB100
      UCF-QNRF1000
      UCF_CC_50100
    • Table 2. Comparison results of the different methods

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      Table 2. Comparison results of the different methods

      MethodSHHASHHBUCF-QNRFUCF_CC_50
      MAEMSEMAEMSEMAEMSEMAEMSE
      MCNN24110.2173.226.441.3243.5364.7467.0498.5
      Switching CNN2690.4135.021.633.4228.0445.0318.1439.2
      CMTL31101.3152.420.031.1252514322.8397.9
      CSRNet1668.2115.010.616.0120.3208.5266.1397.5
      SANet3267.0104.58.413.6----
      ACSPNet1885.2137.115.423.1----
      PCCNet1073.5124.011.019.0149.0247.0240.0315.5
      AMCNN276.1110.715.327.4----
      LSC-CNN3366.4117.08.112.7120.5218.2255.6302.7
      TEDNet3064.2109.18.212.8113.0188.0249.4354.5
      SCLNet2067.9102.99.114.1109.6182.5258.9326.2
      DSPNet1368.2107.88.914.0107.5182.7243.3307.6
      MSCANet3466.5109.4--104.1183.8242.8329.8
      Method in Ref.[1561.9100.57.411.7104.8182.3212.3289.6
      AMS-Net1163.8108.57.311.886.5167.2236.5319.2
      Proposed method61.1101.37.011.3102.5181.7202.0288.7
    • Table 3. Ablation study on the SHHA dataset

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      Table 3. Ablation study on the SHHA dataset

      ComponentMAEMSETime /msParameters /106
      VGG(dilation rate is 2)(baseline)69.1106.2166.5411.54
      VGG+1INV(dilation rate is 2)66.9105.2145.3811.81
      VGG+2INV(dilation rate is 2)64.9101.4133.8512.08
      VGG+3INV(dilation rate is 1)67.0108.5132.2012.35
      VGG+3INV(dilation rate is 2)63.6103.6127.5312.35
      VGG+3INV(dilation rate is 2)+residual connection62.9106.5133.6312.35
      VGG+3INV(dilation rate is 2)+residual connection+Loss61.1101.3136.4312.35
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    Zhaoxin Li, Shuhua Lu, Lingqiang Lan, Qiyuan Liu. Convolutional Neural Network Method for Crowd Counting Improved using Involution Operator[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815004

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    Paper Information

    Category: Machine Vision

    Received: Jun. 21, 2021

    Accepted: Jul. 20, 2021

    Published Online: Aug. 29, 2022

    The Author Email: Shuhua Lu (lushuhua@ppsuc.edu.cn)

    DOI:10.3788/LOP202259.1815004

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