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基于多样化局部注意力网络的行人重识别

徐胜军 刘求缘 史亚 孟月波 刘光辉 韩九强

徐胜军, 刘求缘, 史亚, 孟月波, 刘光辉, 韩九强. 基于多样化局部注意力网络的行人重识别[J]. 电子与信息学报, 2022, 44(1): 211-220. doi: 10.11999/JEIT201003
引用本文: 徐胜军, 刘求缘, 史亚, 孟月波, 刘光辉, 韩九强. 基于多样化局部注意力网络的行人重识别[J]. 电子与信息学报, 2022, 44(1): 211-220. doi: 10.11999/JEIT201003
XU Shengjun, LIU Qiuyuan, SHI Ya, MENG Yuebo, LIU Guanghui, HAN Jiuqiang. Person Re-Identification Based on Diversified Local Attention Network[J]. Journal of Electronics & Information Technology, 2022, 44(1): 211-220. doi: 10.11999/JEIT201003
Citation: XU Shengjun, LIU Qiuyuan, SHI Ya, MENG Yuebo, LIU Guanghui, HAN Jiuqiang. Person Re-Identification Based on Diversified Local Attention Network[J]. Journal of Electronics & Information Technology, 2022, 44(1): 211-220. doi: 10.11999/JEIT201003

基于多样化局部注意力网络的行人重识别

doi: 10.11999/JEIT201003
基金项目: 国家自然科学基金(61803293, 51678470),陕西省自然科学基础研究计划(2020JM472, 2020JM473, 2019JQ760),琶洲实验室基金(2020PZZZPT0002)
详细信息
    作者简介:

    徐胜军:男,1976年生,博士,副教授,研究方向为视觉识别智能感知理论、人工智能与智动化系统等

    刘求缘:男,1996年生,硕士生,研究方向为深度学习、行人重识别等

    史亚:女,1985年生,博士,讲师,研究方向为机器学习、雷达信号分类等

    孟月波:女,1978年生,博士,副教授,研究方向为计算机视觉感知、建筑智能化技术等

    刘光辉:男,1976年生,博士,副教授,研究方向为计算机视觉感知、人工智能与机器人等

    韩九强:男,1951年生,教授,研究方向为视觉识别智能感知理论、人工智能与自动化系统、模型仿真智能控制理论等

    通讯作者:

    刘求缘 18291975902@163.com

  • 中图分类号: TP391.41

Person Re-Identification Based on Diversified Local Attention Network

Funds: The National Natural Science Foundation of China (61803293, 51678470), The Natural Science Basic Research Plan in Shaanxi Province of China (2020JM472, 2020JM473, 2019JQ760), Pa Zhou Laboratory Foundation (2020PZZZPT0002)
  • 摘要: 针对现实场景中行人图像被遮挡以及行人姿态或视角变化造成的未对齐问题,该文提出一种基于多样化局部注意力网络(DLAN)的行人重识别(Re-ID)方法。首先,在骨干网络后分别设计了全局网络和多分支局部注意力网络,一方面学习全局的人体空间结构特征,另一方面自适应地获取人体不同部位的显著性局部特征;然后,构造了一致性激活惩罚函数引导各局部分支学习不同身体区域的互补特征,从而获取行人的多样化特征表示;最后,将全局特征与局部特征集成到分类识别网络中,通过联合学习形成更全面的行人描述。在Market1501, DukeMTMC-reID和CUHK03行人重识别数据集上,DLAN模型的mAP值分别达到了88.4%, 79.5%和74.3%,Rank-1值分别达到了95.1%, 88.7%和76.3%,明显优于大多数现有方法,实验结果充分验证了所提方法的鲁棒性和判别能力。
  • 图  1  DLAN模型架构

    图  2  LAN结构图

    图  3  Market1501数据集上mAP和Rank-1随k值变化曲线图

    图  4  Market1501数据集上mAP和Rank-1随$\gamma $值变化曲线图

    图  5  DLAN模型各分支可视化图

    图  6  主要网络结构可视化图

    图  7  Partial-REID数据集上各算法性能对比图

    表  1  DLAN模型网络变种结构表

    网络变种分支结构损失函数网络变种分支结构损失函数
    全局局部全局局部
    LAN
    BaselineGAP
    BN
    $L_{{\rm{id}}}^{} + L_{{\rm{tri}}}^{}$3L+LAN+CAPGAP
    BN
    CAP$L_{{\rm{id}}}^{}{\rm{ + }}L_{{\rm{tri}}}^{}{\rm{ + }}{L_{{\rm{CAP}}}}$
    3LGAP
    BN
    $L_{{\rm{id}}}^{} + L_{{\rm{tri}}}^{}$G+3L+LANGAP
    BN
    LAN
    GAP
    BN
    $L_{{\rm{id}}}^{} + L_{{\rm{tri}}}^{}$
    LAN
    3L+LANLAN
    GAP
    BN
    $L_{{\rm{id}}}^{} + L_{{\rm{tri}}}^{}$G+3L+LAN+CAP
    (DLAN)
    GAP
    BN
    GAP
    BN
    CAP$L_{{\rm{id}}}^{}{\rm{ + }}L_{{\rm{tri}}}^{}{\rm{ + }}{L_{{\rm{CAP}}}}$
    下载: 导出CSV

    表  2  消融实验结果(%)

    网络变种mAPRank-1Rank-5Rank-10Params
    Baseline84.393.597.598.925.1M
    3L85.994.098.399.038.2M
    3L+LAN86.394.398.499.043.5M
    3L+LAN+CAP86.994.798.499.043.5M
    G+3L+LAN87.894.798.599.049.6M
    G+3L+LAN+CAP(DLAN)88.495.198.699.149.6M
    下载: 导出CSV

    表  3  DLAN模型及各对比算法在不同遮挡水平下的重识别结果(%)

    Market1501DukeMTMC-reID
    s = 0s = 0.3s = 0.6s = 0s = 0.3s = 0.6
    Rank-1mAPRank-1mAPRank-1mAPRank-1mAPRank-1mAPRank-1mAP
    XQDA43.021.728.328.328.312.031.217.220.510.617.49.4
    NPD55.430.039.619.132.516.146.727.333.717.729.715.7
    IDE81.961.062.448.245.636.466.345.257.941.639.028.4
    TriNet83.264.968.654.747.938.971.451.656.040.843.128.4
    P2S69.950.136.227.035.825.958.740.545.231.533.522.9
    PAN81.063.452.036.543.230.071.651.544.729.039.925.9
    SVDNet81.461.262.346.952.040.375.956.359.143.550.637.9
    RandEra85.868.473.858.751.438.973.35762.947.447.935.1
    RNLSTMA90.676.977.064.053.145.177.462.570.258.652.341.5
    mGD+RNLSTMA91.377.985.171.265.853.280.863.974.158.863.047.7
    DLAN95.188.489.279.371.559.088.779.583.672.468.956.6
    下载: 导出CSV

    表  4  DLAN方法与现有Re-ID方法的性能比较(%)

    方法Market1501DukeMTMC-REIDCUHK03-NP-LabledCUHK03-NP-Detected
    mAPRank-1mAPRank-1mAPRank-1mAPRank-1
    SVDNet[29]62.182.356.876.737.341.5
    SGGNN[30]82.892.368.281.1
    PCB[8]81.693.869.283.357.563.7
    BDB[32]84.394.272.186.871.773.669.372.8
    CAM[14]84.594.773.787.7
    MHN[31]85.095.177.289.172.477.265.471.7
    CCAN[22]87.094.676.887.272.975.270.773.0
    DLAN88.495.179.588.774.376.371.873.4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-11-30
  • 修回日期:  2021-07-05
  • 网络出版日期:  2021-08-19
  • 刊出日期:  2022-01-10

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