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基于双向参考集矩阵度量学习的行人再识别

陈莹 许潇月

陈莹, 许潇月. 基于双向参考集矩阵度量学习的行人再识别[J]. 电子与信息学报, 2020, 42(2): 394-402. doi: 10.11999/JEIT190159
引用本文: 陈莹, 许潇月. 基于双向参考集矩阵度量学习的行人再识别[J]. 电子与信息学报, 2020, 42(2): 394-402. doi: 10.11999/JEIT190159
Ying CHEN, Xiaoyue XU. Matrix Metric Learning for Person Re-identification Based on Bidirectional Reference Set[J]. Journal of Electronics and Information Technology, 2020, 42(2): 394-402. doi: 10.11999/JEIT190159
Citation: Ying CHEN, Xiaoyue XU. Matrix Metric Learning for Person Re-identification Based on Bidirectional Reference Set[J]. Journal of Electronics and Information Technology, 2020, 42(2): 394-402. doi: 10.11999/JEIT190159

基于双向参考集矩阵度量学习的行人再识别

doi: 10.11999/JEIT190159
基金项目: 国家自然科学基金(61573168)
详细信息
    作者简介:

    陈莹:女,1976年生,教授,博士生导师,研究方向为模式识别、信息融合

    许潇月:女,1994年生,硕士生,研究方向为行人再识别

    通讯作者:

    陈莹 chenying@jiangnan.edu.cn

  • 中图分类号: TN911.73; TP391

Matrix Metric Learning for Person Re-identification Based on Bidirectional Reference Set

Funds: The National Natural Science Foundation of China (61573168)
  • 摘要: 针对行人再识别中由于外观差异不显著导致特征描述不准确的问题,该文提出一种基于双向参考集矩阵度量学习(BRM2L)的行人再识别算法。首先通过互近邻算法获得每个摄像头下的互近邻参考集,为保证参考集的鲁棒性,联合考虑各摄像头下的互近邻参考集获得双向参考集。通过双向参考集挖掘出困难样本进行特征描述,从而得到准确的外观差异描述。最后利用该特征描述进行更有效的矩阵度量学习。在多个公开数据集上的实验结果证明了该算法比现有算法具有更好的行人再识别性能。
  • 图  1  差异矩阵描述子

    图  2  算法框架图

    图  3  双向参考集与随机参考集结果对比图

    表  1  两个摄像头下参考集里的样本标签的重叠率$\sigma $(%)

    行人行人1行人2行人3行人4
    重叠率$\sigma $20501025
    下载: 导出CSV

    表  2  在3个数据集上采用不同特征的匹配精度(%)

    方法VIPeRCHUK01PRID450S
    Rank-1Rank-5Rank-1Rank-5Rank-1Rank-5
    ${{\rm{L}}_{\rm{2}}}$范数(GoG)19.0038.0024.1751.3332.4460.00
    F范数(GoG)20.1741.8334.5069.8352.2280.22
    ${\rm{BR}}{{\rm{M}}^{\rm{2}}}{\rm{L}}$(GoG)38.3369.1745.3370.5054.4480.67
    ${{\rm{L}}_{\rm{2}}}$范数(FCTNN)29.0046.0037.4458.0031.7357.96
    F范数(FCTNN)30.0049.8346.5672.1144.4072.84
    ${\rm{BR}}{{\rm{M}}^{\rm{2}}}{\rm{L}}$(FCTNN)41.3368.1747.4277.4445.5172.96
    下载: 导出CSV

    表  3  VIPeR数据集上的结果

    方法Rank-1Rank-5Rank-10Rank-20
    PCCA[16]19.348.964.980.3
    KISSME[18]19.648.062.277.0
    BiCov[17]20.643.256.168.0
    eSDC[19]26.346.458.672.8
    DeepMetric[24]28.259.373.486.4
    Midfilter[21]29.152.565.979.9
    LADF[20]30.064.080.092.0
    FTCNN[15]+XQDA31.259.874.083.5
    RD[6]33.365.178.388.5
    GoG[14]+XQDA37.367.477.289.6
    SCNCD[22]37.868.581.290.4
    ${\rm{D}}{{\rm{M}}^{\rm{3}}}$[4]38.367.277.089.3
    DeepRanking[25]38.469.281.390.4
    LOMO+XQDA[23]40.068.580.591.0
    DeepList[26]40.569.180.191.2
    ${\rm{BR}}{{\rm{M}}^2}{\rm{L}}$(GoG)38.3369.1781.5089.50
    ${\rm{BR}}{{\rm{M}}^2}{\rm{L}}$(FTCNN)41.3368.1782.0090.33
    下载: 导出CSV

    表  4  PRID 450S数据集上的结果

    方法Rank-1Rank-5Rank-10Rank-20
    KISSME[18]33.059.871.079.0
    CBRA[27]26.457.171.083.2
    CSL[28]44.471.682.289.8
    Mirror[29]55.479.387.893.9
    DRML[30]56.482.290.2
    DM3[4]56.783.188.494.7
    ${\rm{BR}}{{\rm{M}}^2}{\rm{L}}$(GoG)54.4480.6789.7895.56
    ${\rm{BR}}{{\rm{M}}^2}{\rm{L}}$(FTCNN)59.2084.5394.5399.78
    下载: 导出CSV

    表  5  CUHK01数据集上的结果

    方法Rank-1Rank-5Rank-10Rank-20
    SDALF[1]9.922.630.341.0
    TML[12]20.043.556.069.3
    MidFilter[21]34.355.165.074.9
    ImprovedDeep[31]47.571.080.0
    RD[6]31.168.579.1
    ${\rm{D}}{{\rm{M}}^{\rm{3}}}$[4]43.770.177.488.7
    ${\rm{BR}}{{\rm{M}}^2}{\rm{L}}$(GoG)45.3370.5086.5090.00
    ${\rm{BR}}{{\rm{M}}^2}{\rm{L}}$(FTCNN)47.4277.4488.3398.33
    下载: 导出CSV
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    出版历程
    • 收稿日期:  2019-03-18
    • 修回日期:  2019-05-24
    • 网络出版日期:  2019-07-03
    • 刊出日期:  2020-02-19

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