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基于注意力机制的跨分辨率行人重识别

廖华年 徐新

廖华年, 徐新. 基于注意力机制的跨分辨率行人重识别[J]. 北京航空航天大学学报, 2021, 47(3): 605-612. doi: 10.13700/j.bh.1001-5965.2020.0471
引用本文: 廖华年, 徐新. 基于注意力机制的跨分辨率行人重识别[J]. 北京航空航天大学学报, 2021, 47(3): 605-612. doi: 10.13700/j.bh.1001-5965.2020.0471
LIAO Huanian, XU Xin. Cross-resolution person re-identification based on attention mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 605-612. doi: 10.13700/j.bh.1001-5965.2020.0471(in Chinese)
Citation: LIAO Huanian, XU Xin. Cross-resolution person re-identification based on attention mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 605-612. doi: 10.13700/j.bh.1001-5965.2020.0471(in Chinese)

基于注意力机制的跨分辨率行人重识别

doi: 10.13700/j.bh.1001-5965.2020.0471
基金项目: 

国家自然科学基金 U1803262

国家自然科学基金 61602349

国家自然科学基金 61440016

深圳市科技计划基础研究项目 JCYJ20170818143246278

详细信息
    作者简介:

    廖华年  女, 硕士研究生。主要研究方向: 计算机视觉、行人重识别; 徐新, 男, 博士, 教授, 博士生导师。主要研究方向: 计算机视觉、机器学习、行人重识别

    徐新  男,博士,教授,博士生导师。主要研究方向:计算机视 觉、机器学习、行人重识别

    通讯作者:

    徐新, E-mail: xuxin0336@163.com

  • 中图分类号: TP391

Cross-resolution person re-identification based on attention mechanism

Funds: 

National Natural Science Foundation of China U1803262

National Natural Science Foundation of China 61602349

National Natural Science Foundation of China 61440016

Basic Research Project of Science and Technology Plan of Shenzhen JCYJ20170818143246278

More Information
  • 摘要:

    行人图像分辨率的变化对现有的行人重识别方法带来了很大的挑战。针对这一问题,提出了一种新的跨分辨率行人重识别方法。该方法从两方面解决分辨率变化带来的识别困难:一方面通过通道注意力机制和空间注意力机制捕捉人物特征获取局部区域;另一方面通过核动态上采样模块恢复任意分辨率图像的局部区域信息。为了验证所提方法的有效性,在Market1501、CUHK03和CAVIAR三个公开数据集上开展了对比实验,实验结果表明:所提方法取得了最佳性能。

     

  • 图 1  跨分辨率行人图像

    Figure 1.  Cross-resolution pedestrian image

    图 2  注意力网络框架

    Figure 2.  Framework of attention network

    图 3  各模型主观性能对比

    Figure 3.  qSubjective performance comparison of various models

    表  1  现有方法在Market1501和CUHK03数据集上的定量结果对比

    Table  1.   Quantitative result comparison of existing methods on Market1501和CUHK03 datasets   %

    方法 Market1501 CUHK03
    Rank1 Rank5 Rank1 Rank5
    FD-GAN[31] 79.6 91.6 73.4 93.8
    JUDEA[3] 26.2 58.0
    SDF[6] 22.2 48.0
    SING[4] 74.4 87.8 67.7 90.7
    CSR-GAN[5] 76.4 88.5 71.3 92.1
    RAIN[7] 78.9 97.3
    CAD[8] 83.7 92.7 82.1 97.4
    INTACT[11] 88.1 95.0 86.4 97.4
    RIPR[12] 66.9 84.7 73.3 92.6
    本文 90.2 94.3 89.2 97.5
    下载: 导出CSV

    表  2  现有方法在CAVIAR数据集上的定量结果对比

    Table  2.   Quantitative result comparison of existing methods on CAVIAR dataset  %

    方法 Rank1 Rank5 Rank10
    FD-GAN[31] 32.3 72.3 85.9
    SLD2L[2] 18.4 44.8 61.2
    JUDEA[3] 22.0 60.1 80.8
    SDF[6] 14.3 37.5 62.5
    SING[4] 33.5 72.7 89.0
    CSR-GAN[5] 34.7 72.5 87.4
    RAIN[7] 42.0 77.3 89.6
    CAD[8] 42.8 76.2 91.5
    INTACT[11] 44.0 81.8 93.9
    RIPR[12] 36.4 72.0 90.0
    本文 49.3 83.7 91.2
    下载: 导出CSV

    表  3  各模块消融实验结果

    Table  3.   Ablation experimental results of each module   %

    模块 MLRCUHK03
    Rank1 Rank5
    ResNet50 58.1 79.3
    ResNet50+CAM 63.2 85.0
    ResNet50+SAM 65.0 87.1
    ResNet50+NonLocal 70.7 87.7
    ResNet50+SENet 71.3 89.1
    ResNet50+CAM+SAM 78.9 90.3
    ResNet50+MASR+ID 83.3 93.1
    本文 89.2 97.5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-08-28
  • 录用日期:  2020-09-18
  • 刊出日期:  2021-03-20

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