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基于局部语义特征不变性的跨域行人重识别

张晓伟 吕明强 李慧

张晓伟, 吕明强, 李慧等 . 基于局部语义特征不变性的跨域行人重识别[J]. 北京航空航天大学学报, 2020, 46(9): 1682-1690. doi: 10.13700/j.bh.1001-5965.2020.0072
引用本文: 张晓伟, 吕明强, 李慧等 . 基于局部语义特征不变性的跨域行人重识别[J]. 北京航空航天大学学报, 2020, 46(9): 1682-1690. doi: 10.13700/j.bh.1001-5965.2020.0072
ZHANG Xiaowei, LYU Mingqiang, LI huiet al. Cross-domain person re-identification based on partial semantic feature invariance[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(9): 1682-1690. doi: 10.13700/j.bh.1001-5965.2020.0072(in Chinese)
Citation: ZHANG Xiaowei, LYU Mingqiang, LI huiet al. Cross-domain person re-identification based on partial semantic feature invariance[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(9): 1682-1690. doi: 10.13700/j.bh.1001-5965.2020.0072(in Chinese)

基于局部语义特征不变性的跨域行人重识别

doi: 10.13700/j.bh.1001-5965.2020.0072
基金项目: 

国家自然科学基金 61902204

山东省自然科学基金 ZR2019BF028

详细信息
    作者简介:

    张晓伟  男,博士,讲师。主要研究方向:图像/视频分析和理解、计算机视觉和机器学习

    吕明强  男,本科生。主要研究方向:行人重识别

    李慧  女,硕士研究生。主要研究方向:行人重识别和计算机视觉

    通讯作者:

    张晓伟.E-mail: by1306114@buaa.edu.cn

  • 中图分类号: TP37;TP277

Cross-domain person re-identification based on partial semantic feature invariance

Funds: 

National Natural Science Foundation of China 61902204

Natural Science Foundation of Shandong Province of China ZR2019BF028

More Information
  • 摘要:

    行人重识别是刑侦案件中重要的侦查手段,而跨域是行人重识别的主要挑战之一,也是制约其实际应用的瓶颈问题。在带标签的源域和无标签的目标域学习跨域行人局部语义不变性特征模型。首先,在源域上通过只含有行人标识无部件标签的监督学习方式学习行人的各部件特征,并在源域和目标域上采用无监督学习方式对齐行人部件。然后,基于对齐后的行人全局与局部特征,引入特征模板池存储对齐后的目标域全局和局部特征,并设计了跨域不变性损失函数进行特征不变性约束,提高行人重识别的跨域适应能力。最后,在Market-1501、DukeMTMC-reID和MSMT17数据集之间开展了跨域行人重识别验证实验,实验结果表明,所提方法在跨域行人重识别上取得了显著的性能提升。

     

  • 图 1  基于局部语义特征不变性的跨域行人重识别框架

    Figure 1.  Framework of cross-domain person re-identification based on partial semantic feature invariance

    图 2  基于语义特征的行人部件示意图

    Figure 2.  Schematic diagram of person parts

    表  1  在Market-1501和DukeMTMC-reID数据集上的跨域行人重识别消融实验

    Table  1.   Ablation experiment of cross-domain person re-identification on Market-1501 and DukeMTMC-reID datasets

    行人重识别跨域方法 DukeMTMC-reID→Market-1501 Market-1501→DukeMTMC-reID
    R-1/% R-5/% R-10/% R-20/% mAP/% R-1/% R-5/% R-10/% R-20/% mAP/%
    GAP 45.1 62.4 68.9 75.2 18.8 29.6 43.9 50.2 57.0 15.1
    PAF 45.9 63.5 69.9 75.7 19.7 30.0 45.4 51.8 58.1 15.5
    GAP+PAF 46.6 63.9 70.6 76.3 20.4 30.3 46.7 52.4 58.8 16.0
    Inv+GAP 65.7 81.5 86.0 89.9 31.9 52.7 64.7 69.7 73.4 27.0
    Inv+PAF 75.7 89.4 92.0 94.3 43.5 64.1 76.5 80.3 83.8 41.0
    Inv+GAP+PAF 77.6 88.7 92.0 95.2 45.0 65.5 77.6 81.1 84.5 42.8
    行人重识别单域方法 DukeMTMC-reID→Market-1501 Market-1501→DukeMTMC-reID
    R-1/% R-5/% R-10/% R-20/% mAP/% R-1/% R-5/% R-10/% R-20/% mAP/%
    Inv+GAP+PAF 74.7 85.5 89.1 91.8 54.1 88.2 95.6 97.4 98.3 67.0
    下载: 导出CSV

    表  2  特征模板池与Mini-batch的比较

    Table  2.   Comparison of feature template pooling memory and Mini-batch

    方法 DukeMTMC-reID→Market-1501
    R-1/% R-5/% R-10/% R-20/% mAP/%
    Mini-batch 66.4 83.5 88.3 90.6 36.2
    Mini-batch+Memory 77.6 88.7 92.0 95.2 45.0
    下载: 导出CSV

    表  3  在Market-1501和DukeMTMC-reID到MSMT17跨域数据集上与当前先进方法的实验比较

    Table  3.   Experimental comparison with other advanced methods from Market-1501 and DukeMTMC-reID to MSMT17 cross-domain datasets

    方法 Market-1501→MSMT17 DukeMTMC-reID→MSMT17
    R-1/% R-5/% R-10/% mAP/% R-1/% R-5/% R-10/% mAP/%
    PTGAN[30] 10.2 24.4 2.9 11.8 27.4 3.3
    ECN[44] 25.3 36.3 42.1 8.5 30.2 41.5 46.8 10.2
    本文 25.6 37.1 43.3 8.9 30.6 41.8 47.6 10.7
    下载: 导出CSV

    表  4  在Market-1501和DukeMTMC-reID跨域数据集上与当前先进方法的实验比较

    Table  4.   Experimental comparison with other advanced methods on Market-1501 and DukeMTMC-reID cross-domain datasets

    行人重识别跨域方法 DukeMTMC-reID →Market-1501 Market-1501→DukeMTMC-reID
    R-1/% R-5/% R-10/% mAP/% R-1/% R-5/% R-10/% mAP/%
    MMFA[33] 56.7 75.0 81.8 27.4 45.3 59.8 66.3 24.7
    SPGAN+LM[29] 57.7 75.8 82.4 26.7 46.4 62.3 68.0 26.2
    TJ-AIDL[32] 58.2 74.8 81.1 26.5 44.3 59.6 65.0 23.0
    CamStyle[45] 58.8 78.2 84.3 27.4 48.4 62.5 68.9 25.1
    HHL[46] 62.2 78.8 84.0 31.4 46.9 61.0 66.7 27.2
    ECN[44] 75.1 87.6 91.6 43.0 63.3 75.8 80.4 40.4
    本文 77.6 88.7 92.0 45.0 65.5 77.6 81.1 42.8
    下载: 导出CSV
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  • 收稿日期:  2020-03-02
  • 录用日期:  2020-03-28
  • 网络出版日期:  2020-09-20

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