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基于多尺度和注意力融合学习的行人重识别

王粉花 赵波 黄超 严由齐

王粉花, 赵波, 黄超, 严由齐. 基于多尺度和注意力融合学习的行人重识别[J]. 电子与信息学报, 2020, 42(12): 3045-3052. doi: 10.11999/JEIT190998
引用本文: 王粉花, 赵波, 黄超, 严由齐. 基于多尺度和注意力融合学习的行人重识别[J]. 电子与信息学报, 2020, 42(12): 3045-3052. doi: 10.11999/JEIT190998
Fenhua WANG, Bo ZHAO, Chao HUANG, Youqi YAN. Person Re-identification Based on Multi-scale Network Attention Fusion[J]. Journal of Electronics and Information Technology, 2020, 42(12): 3045-3052. doi: 10.11999/JEIT190998
Citation: Fenhua WANG, Bo ZHAO, Chao HUANG, Youqi YAN. Person Re-identification Based on Multi-scale Network Attention Fusion[J]. Journal of Electronics and Information Technology, 2020, 42(12): 3045-3052. doi: 10.11999/JEIT190998

基于多尺度和注意力融合学习的行人重识别

doi: 10.11999/JEIT190998
基金项目: 国家重点研发计划重点专项(2017YFB1400101-01),北京科技大学中央高校基本科研业务费专项 (FRF-BD-19-002A)
详细信息
    作者简介:

    王粉花:女,1971年生,副教授,硕士生导师,研究方向为模式识别和智能信息处理

    赵波:男,1994年生,硕士生,研究方向为计算机视觉

    黄超:男,1993年生,硕士生,研究方向为计算机视觉

    严由齐:男,1997年生,硕士生,研究方向为计算机视觉

    通讯作者:

    王粉花 wangfenhua@ustb.edu.cn

  • 中图分类号: TN911.73; TP391

Person Re-identification Based on Multi-scale Network Attention Fusion

Funds: The Key Projects of National Key R & D Plan (2017YFB1400101-01), Beijing University of Science and Technology Central University Basic Research Business Expenses (FRF-BD-19-002A)
  • 摘要: 行人重识别的关键依赖于行人特征的提取,卷积神经网络具有强大的特征提取以及表达能力。针对不同尺度下可以观察到不同的特征,该文提出一种基于多尺度和注意力网络融合的行人重识别方法(MSAN)。该方法通过对网络不同深度的特征进行采样,将采样的特征融合后对行人进行预测。不同深度的特征图具有不同的表达能力,使网络可以学习到行人身上更加细粒度的特征。同时将注意力模块嵌入到残差网络中,使得网络能更加关注于一些关键信息,增强网络特征学习能力。所提方法在Market1501, DukeMTMC-reID和MSMT17_V1数据集上首位准确率分别到了95.3%, 89.8%和82.2%。实验表明,该方法充分利用了网络不同深度的信息和关注的关键信息,使模型具有很强的判别能力,而且所提模型的平均准确率优于大多数先进算法。
  • 图  1  多尺度和注意力融合模型框架图

    图  2  ResNet50网络架构图

    图  3  Conv2_x模块架构图

    图  4  多尺度结构图

    图  5  CBAM模块图

    图  6  3元组损失

    表  1  多尺度融合模型准确率验证实验结果(%)

    方法Market1501DukeMTMC-reIDMSMT17_V1
    Rank-1mAPRank-1mAPRank-1mAP
    SSAN94.987.986.167.781.466.3
    SSAN(+RK)95.393.786.075.684.673.8
    MSAN95.387.989.878.882.260.6
    MSAN (+RK)95.993.992.389.785.074.6
    下载: 导出CSV

    表  2  CBAM模块准确率验证实验结果(%)

    方法Market1501DukeMTMC-reIDMSMT17_V1
    Rank-1mAPRank-1mAPRank-1mAP
    MSN94.486.287.577.279.656.0
    MSN (+CBAM)95.387.989.878.882.260.6
    MSN(+RK)95.393.190.989.283.272.0
    MSN(+CBAM+RK)95.993.992.389.785.074.6
    下载: 导出CSV

    表  3  所提MSAN算法与其他先进算法的准确率对比(%)

    方法Market1501DukeMTMC-reIDMSMT17_V1
    Rank-1mAPRank-1mAPRank-1mAP
    SVDNet[21]82.362.176.756.8
    DPFL[22]88.672.679.260.0
    SVDNet+Era[23]87.171.379.362.4
    TriNET+Era[23]83.968.773.056.6
    DaRe[24]89.076.080.264.5
    GP-reid[25]92.281.285.272.8
    PCB[4]92.377.481.965.368.240.4
    Aligned-ReID[5]92.682.3
    PCB+RPP[4]93.881.683.369.2
    MGN[6]95.786.988.778.4
    BFENET[8]94.284.386.872.1
    IANet[18]94.483.187.173.475.546.8
    DGNet[19]94.886.086.674.877.252.3
    OSNet[20]94.884.988.673.578.752.9
    MSAN95.387.989.878.882.260.6
    MSAN(+RK)95.993.992.389.785.074.6
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-12-13
  • 修回日期:  2020-06-17
  • 网络出版日期:  2020-07-20
  • 刊出日期:  2020-12-08

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