• Opto-Electronic Engineering
  • Vol. 51, Issue 10, 240174 (2024)
Hongmin Zhang*, Qianqian Tian, Dingding Yan, and Lingyu Bu
Author Affiliations
  • School of Electrical and Electronic Engineering,Chongqing University of Technology,Chongqing 400054,China
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    DOI: 10.12086/oee.2024.240174 Cite this Article
    Hongmin Zhang, Qianqian Tian, Dingding Yan, Lingyu Bu. GLCrowd: a weakly supervised global-local attention model for congested crowd counting[J]. Opto-Electronic Engineering, 2024, 51(10): 240174 Copy Citation Text show less
    GLCrowd network structure
    Fig. 1. GLCrowd network structure
    Global-local attention module
    Fig. 2. Global-local attention module
    Comparison of different methods. (a) Traditional convolution; (b) Standard self-attention; (c) Local attention
    Fig. 3. Comparison of different methods. (a) Traditional convolution; (b) Standard self-attention; (c) Local attention
    ConvFFN moudle
    Fig. 4. ConvFFN moudle
    Partial visualization results
    Fig. 5. Partial visualization results
    Shanghai TechUCF-QNRFUCF_CC_50
    Part APart B
    图像数量482716153550
    平均尺寸589×868768×10242013×29022101×2888
    平均人数501123.612791278
    最大人数3139578128654543
    总人数24167788488125164263974
    Table 1. Information of datasets
    配置参数
    操作系统Ubuntu 20.04.3 LTS (GNU/Linux 5.15.0-97-generic x86_64)
    显卡型号NVIDIA GeForce RTX 4090(×1)
    显存大小24 G
    Table 2. Information of experimental environment
    实验参数具体参数
    权重衰减5×10−4
    训练总周期数1200
    优化器动量0.95
    学习率1×10−5
    Table 3. Data of partial experimental parameters
    算法模型Part APart B
    MAEMSEMAEMSE
    HLNet[40]71.5108.611.320
    Transcrowd_gap[29]66.1105.19.316.1
    Transcrowd_token[29]69.0116.510.619.7
    MATT[41]80.1129.411.717.5
    OT_M[20]70.7114.58.113.1
    SUA_crowd[42]68.5121.914.120.6
    PDDNet[43]72.6112.210.317.0
    GLCrowd64.887104.4118.95816.202
    Table 4. Experimental results on Shanghai Tech dataset
    算法模型MAEMSE
    HLNet[40]100.4182.6
    Transcrowd_token[29]98.9176.1
    Transcrowd_gap[29]97.2168.5
    OT_M[20]100.6167.6
    SUA_crowd[42]130.3226.3
    PDDNet[43]130.2246.6
    GLCrowd95.523173.453
    Table 5. Experimental results on UCF_QNRF dataset
    算法模型MAEMSE
    MATT[41]355.0550.0
    CCTrans[31]245.0343.0
    SSGP_Crowd[44]355.0505.0
    SE Cycle GAN[45]373.4528.8
    CDPL_crowd[46]336.5486.1
    Transcrowd[29]272.2395.3
    CrowdFormer[47]218.8330.4
    PC-Net[48]217.3309.7
    GLCrowd209.660282.217
    Table 6. Experimental results on UCF_CC_50 dataset
    局部 注意力ConvFFN回归 令牌Part APart BUCF_CC_50
    MAEMSEMAEMSEMAEMSE
    第一组××75.260125.44110.00319.315240.120327.796
    第二组×67.403108.2989.42818.446236.233305.122
    第三组64.887104.4118.95516.202209.660282.217
    Table 7. Comparative results of ablation experiments
    Hongmin Zhang, Qianqian Tian, Dingding Yan, Lingyu Bu. GLCrowd: a weakly supervised global-local attention model for congested crowd counting[J]. Opto-Electronic Engineering, 2024, 51(10): 240174
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