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基于空间注意力机制的行人再识别方法

张子昊 周千里 王蓉

张子昊, 周千里, 王蓉等 . 基于空间注意力机制的行人再识别方法[J]. 北京航空航天大学学报, 2020, 46(9): 1747-1755. doi: 10.13700/j.bh.1001-5965.2020.0075
引用本文: 张子昊, 周千里, 王蓉等 . 基于空间注意力机制的行人再识别方法[J]. 北京航空航天大学学报, 2020, 46(9): 1747-1755. doi: 10.13700/j.bh.1001-5965.2020.0075
ZHANG Zihao, ZHOU Qianli, WANG Ronget al. Pedestrian re-identification method based on spatial attention mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(9): 1747-1755. doi: 10.13700/j.bh.1001-5965.2020.0075(in Chinese)
Citation: ZHANG Zihao, ZHOU Qianli, WANG Ronget al. Pedestrian re-identification method based on spatial attention mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(9): 1747-1755. doi: 10.13700/j.bh.1001-5965.2020.0075(in Chinese)

基于空间注意力机制的行人再识别方法

doi: 10.13700/j.bh.1001-5965.2020.0075
基金项目: 

国家重点研发计划 A19808

中央高校基本科研业务费专项资金 2019JKF111

详细信息
    作者简介:

    张子昊  男, 硕士研究生。主要研究方向:模式识别、人工智能

    周千里  男, 博士研究生。主要研究方向:模式识别、人工智能

    王蓉  女, 博士, 教授, 博士生导师。主要研究方向:模式识别、人工智能

    通讯作者:

    王蓉, E-mail: dbdxwangrong@163.com

  • 中图分类号: O235;TP183

Pedestrian re-identification method based on spatial attention mechanism

Funds: 

National Key R & D Program of China A19808

the Fundamental Research Funds for the Central Universities 2019JKF111

More Information
    Corresponding author: WANG Rong, E-mail: dbdxwangrong@163.com
  • 摘要:

    行人再识别是图像检索领域的一个重要部分,但是由于行人姿态各异、背景复杂等因素,导致提取到的行人特征鲁棒性和代表性不强,进而影响行人再识别的精度。在AlignedReID++算法基础上,提出了基于空间注意力机制的行人特征提取方法,应用在行人再识别中取得了很好的效果。首先,在特征提取部分,引入空间注意力机制来增强特征表达,同时抑制可能的噪声;其次,通过在卷积层中引入实例正则化层(IN)来辅助批正则化层(BN)对特征进行归一化处理,解决单一BN层对特征色调变化以及光照变化的不敏感性,提高特征提取对亮度、色调变化的鲁棒性;最后,在Market1501、DukeMTMC和CUHK03 3个行人再识别通用数据集上对所提改进模型进行测试评价。实验结果显示:改进后的模型在3个数据集上识别精度分别提升了2%、2.9%和5.1%,表明改进后的模型相较于改进前的模型,在精度以及鲁棒性上都有显著提高。

     

  • 图 1  AlignedReID++算法流程图

    Figure 1.  AlignedReID ++ algorithm flowchart

    图 2  改进后AlignedReID++算法流程图

    Figure 2.  Improved AlignedReID ++ algorithm flowchart

    图 3  SGE-ResNet模块结构

    Figure 3.  SGE-ResNet module structure

    图 4  IBN模块结构

    Figure 4.  IBN module structure

    图 5  4种网络在Market1501、DukeMTMC、CUHK03数据集上的CMC曲线

    Figure 5.  CMC curves of four networks on Market1501, DukeMTMC and CUHK03 datasets

    图 6  不同网络下相似度最高的10张行人图片

    Figure 6.  Ten most similar pedestrian images on different networks

    表  1  基于SGE模块改进的模型性能在Market1501、DukeMTMC、CUHK03数据集上的评价

    Table  1.   Evaluation of improved model performance based on SGE module on Market1501, DukeMTMC and CUHK03 datasets  %

    网络结构 Market1501 DukeMTMC CUHK03
    Rank1/Rank1(RK) mAP/mAP(RK) Rank1/Rank1(RK) mAP/mAP(RK) Rank1/Rank1(RK) mAP/mAP(RK)
    ResNet50 91.0/92.0 77.6/88.5 80.7/85.2 68.0/81.2 60.9/67.6 59.7/70.7
    SGE-ResNet50 90.5/92.4 76.8/88.8 81.5/85.7 68.2/82.7 61.9/71.1 59.9/73.5
    下载: 导出CSV

    表  2  基于IBN层改进的模型性能在Market1501、DukeMTMC、CUHK03数据集上的评价

    Table  2.   Evaluation of improved model performance based on IBN layer on Market1501, DukeMTMC and CUHK03 datasets  %

    网络结构 Market1501 DukeMTMC CUHK03
    Rank1/Rank1(RK) mAP/mAP(RK) Rank1/Rank1(RK) mAP/mAP(RK) Rank1/Rank1(RK) mAP/mAP(RK)
    ResNet50 91.0/92.0 77.6/88.5 80.7/85.2 68.0/81.2 60.9/67.6 59.7/70.7
    IBN-ResNet50 91.2/92.5 79.5/89.7 83.6/87.1 70.4/83.9 65.1/73.1 62.7/75.2
    下载: 导出CSV

    表  3  基于IBN层以及SGE模块改进的模型性能在Market1501、DukeMTMC、CUHK03数据集上的评价

    Table  3.   Evaluation of improved model performance based on IBN layer and SGE module on Market1501, DukeMTMC and CUHK03 datasets  %

    网络结构 Market1501 DukeMTMC CUHK03
    Rank1/Rank1(RK) mAP/mAP(RK) Rank1/Rank1(RK) mAP/mAP(RK) Rank1/Rank1(RK) mAP/mAP(RK)
    ResNet50 91.0/92.0 77.6/88.5 80.7/85.2 68.0/81.2 60.9/67.6 59.7/70.7
    SGE-IBN-ResNet50 91.8/92.9 80.3/90.5 83.6/87.4 71.0/84.1 65.6/73.0 62.9/75.8
    下载: 导出CSV

    表  4  改进后的方法与最新行人再识别方法比较

    Table  4.   Comparison between improved method and latest pedestrian re-identification methods  %

    方法 Market1501 DukeMTMC CUHK03
    Rank1 mAP Rank1 mAP Rank1 mAP
    SVD[20] 82.3 62.1 76.7 56.8 41.5 37.3
    PCE & ECN[21] 87.0 69.0 79.8 62.0 30.2 27.3
    MLFN[22] 90.0 74.3 81.0 62.8 52.8 47.8
    HA-CNN[23] 91.2 75.7 80.5 63.8 41.7 38.3
    AlignedReID++ 91.0 77.6 80.7 68.0 60.9 59.7
    AlignedReID++(RK) 92.0 88.5 85.2 81.2 67.6 70.7
    AlignedReID++(SGE+IBN) 91.8 80.3 83.6 71.0 65.6 62.9
    AlignedReID++(SGE+IBN)(RK) 92.9 90.5 87.4 84.1 73.0 75.8
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-03-03
  • 录用日期:  2020-03-27
  • 刊出日期:  2020-09-20

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