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基于通道注意力机制的行人重识别方法

孙义博 张文靖 王蓉 李冲 张琪

孙义博, 张文靖, 王蓉, 等 . 基于通道注意力机制的行人重识别方法[J]. 北京航空航天大学学报, 2022, 48(5): 881-889. doi: 10.13700/j.bh.1001-5965.2020.0684
引用本文: 孙义博, 张文靖, 王蓉, 等 . 基于通道注意力机制的行人重识别方法[J]. 北京航空航天大学学报, 2022, 48(5): 881-889. doi: 10.13700/j.bh.1001-5965.2020.0684
SUN Yibo, ZHANG Wenjing, WANG Rong, et al. Pedestrian re-identification method based on channel attention mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(5): 881-889. doi: 10.13700/j.bh.1001-5965.2020.0684(in Chinese)
Citation: SUN Yibo, ZHANG Wenjing, WANG Rong, et al. Pedestrian re-identification method based on channel attention mechanism[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(5): 881-889. doi: 10.13700/j.bh.1001-5965.2020.0684(in Chinese)

基于通道注意力机制的行人重识别方法

doi: 10.13700/j.bh.1001-5965.2020.0684
基金项目: 

国家自然科学基金 62076246

中央高校基本科研业务费专项资金 2019JKF426

详细信息
    通讯作者:

    李冲, E-mail: lichong7564@163.com

  • 中图分类号: O235; TP18

Pedestrian re-identification method based on channel attention mechanism

Funds: 

National Natural Science Foundation of China 62076246

the Fundamental Research Funds for the Central Universities 2019JKF426

More Information
  • 摘要:

    针对行人特征表达不充分的问题,提出了一种基于通道注意力机制的行人重识别方法。将通道注意力机制SE模块嵌入到骨干网络ResNet50中,对关键特征信息进行加权强化;采用动态激活函数,根据输入特征动态调整ReLU的参数,增强网络模型的非线性表达能力;将梯度中心化算法引入Adam优化器,提升网络模型的训练速度和泛化能力。在Market1501、DukeMTMC-ReID和CUHK03主流数据集上对改进后的模型进行测试评价,Rank-1分别提升2.17%、2.38%和3.50%,mAP分别提升3.07%、3.39%和4.14%。结果表明:改进后的模型能够提取更强鲁棒性的行人表达特征,达到更高的识别精度。

     

  • 图 1  SCPNet网络模型

    Figure 1.  SCPNet network model

    图 2  基于SCPNet网络模型改进的行人重识别框架

    Figure 2.  Improved pedestrian re-identification framework based on SCPNet network model

    图 3  SE-ResNet结构

    Figure 3.  SE-ResNet structure

    图 4  动态激活函数

    Figure 4.  Dynamic activation function

    图 5  Dynamic ReLU(仅空间位置共享)

    Figure 5.  Dynamic ReLU (spatial-shared and channel-wiseonly)

    图 6  梯度中心化

    Figure 6.  Gradient centralization

    图 7  引入梯度中心化后模型在数据集Market1501、DukeMTMC-ReID、CUHK03上的训练损失

    Figure 7.  Training loss of models with gradient centralization on Market1501, DukeMTMC-ReID, and CUHK03 datasets

    图 8  改进后模型在数据集Market1501、DukeMTMC-ReID、CUHK03上的CMC评价曲线

    Figure 8.  CMC evaluation curves of improved models on Market1501, DukeMTMC-ReID, and CUHK03 datasets

    图 9  改进后模型在Market1501数据集上的查询结果

    Figure 9.  Query results of improved models on Market1501 dataset

    表  1  数据集情况

    Table  1.   Datasets condition

    数据集 公开年份 摄像机个数 ID数 图像数量/张
    Market1501 2015 6 1 501 32 668
    DukeMTMC-ReID 2017 8 1 404 36 411
    CUHK03 2014 10 1 467 13 164
    下载: 导出CSV

    表  2  嵌入SE模块后模型在数据集Market1501、DukeMTMC-ReID和CUHK03上的性能比较

    Table  2.   Performance comparison of models embedded with SE module on Market1501, DukeMTMC-ReID, and CUHK03 datasets  %

    方法 Market1501 DukeMTMC-ReID CUHK03
    Rank-1 mAP Rank-1 mAP Rank-1 mAP
    基线 90.23 76.85 81.60 65.11 58.50 56.34
    基线+SE 91.15 77.85 82.81 67.17 60.07 58.23
    下载: 导出CSV

    表  3  改进激活函数后模型在数据集Market1501、DukeMTMC-ReID和CUHK03上的性能比较

    Table  3.   Performance comparison of models with improved activation functions on Market1501, DukeMTMC-ReID, and CUHK03 datasets  %

    方法 Market1501 DukeMTMC-ReID CUHK03
    Rank-1 mAP Rank-1 mAP Rank-1 mAP
    基线 90.23 76.85 81.60 65.11 58.50 56.34
    基线+DyReLU 91.21 78.90 83.75 67.66 61.50 59.01
    下载: 导出CSV

    表  4  引入梯度中心化后模型在数据集Market1501、DukeMTMC-ReID和CUHK03上的性能比较

    Table  4.   Performance comparison of models with gradient centralization on Market1501, DukeMTMC-ReID, and CUHK03 datasets  %

    方法 Market1501 DukeMTMC-ReID CUHK03
    Rank-1 mAP Rank-1 mAP Rank-1 mAP
    基线 90.23 76.85 81.60 65.11 58.50 56.34
    基线+GC 90.38 77.05 82.05 66.28 59.71 56.63
    下载: 导出CSV

    表  5  在数据集Market1501、DukeMTMC-ReID和CUHK03上的消融实验结果

    Table  5.   Results of ablation experiments on Market1501, DukeMTMC-ReID, and CUHK03 datasets  %

    方法 Market1501 DukeMTMC-ReID CUHK03
    Rank-1 mAP Rank-1 mAP Rank-1 mAP
    基线 90.23 76.85 81.60 65.11 58.50 56.34
    基线+SE 91.15 77.85 82.81 67.17 60.07 58.23
    基线+DyReLU 91.21 78.90 83.75 67.66 61.50 59.01
    基线+GC 90.38 77.05 82.05 66.28 59.71 56.63
    基线+SE+DyReLU 91.39 78.57 83.62 67.55 58.93 56.62
    基线+SE+GC 90.68 78.15 83.39 67.72 59.93 57.79
    基线+DyReLU+GC 92.40 79.92 83.98 68.50 62.00 60.48
    基线+SE+DyReLU+GC 91.09 78.31 83.39 68.03 59.86 58.58
    下载: 导出CSV

    表  6  不同模型在数据集Market1501、DukeMTMC-ReID和CUHK03上的性能比较

    Table  6.   Performance comparison of different models on Market1501, DukeMTMC-ReID, and CUHK03 datasets  %

    方法 Market1501 DukeMTMC-ReID CUHK03
    Rank-1 mAP Rank-1 mAP Rank-1 mAP
    SVDnet[20] 82.3 62.1 76.7 56.8 41.5 37.3
    PAN[21] 82.81 63.35 71.59 51.51 36.29 34.00
    PDC[22] 84.14 63.41 78.29
    AACN[23] 85.90 66.87 76.84 59.25 79.14 78.37
    GLAD[24] 89.9 73.9 82.2
    HA-CNN[25] 91.2 75.7 80.5 63.8 41.7 38.6
    SCPNet 90.23 76.85 81.60 65.11 58.50 56.34
    SCPNet+DyReLU+GC 92.40 79.92 83.98 68.50 62.00 60.48
    SCPNet+SE+DyReLU+GC 91.09 78.31 83.39 68.03 59.86 58.58
    下载: 导出CSV

    表  7  泛化能力比较

    Table  7.   Comparison of generalization ability  %

    方法 Market1501 DukeMTMC-ReID CUHK03
    Rank-1 mAP Rank-1 mAP Rank-1 mAP
    SVDnet[20] 82.3 62.1 76.7 56.8 41.5 37.3
    SVDnet+SE+DyReLU+GC 82.9 63.3 77.8 58.9 42.3 38.7
    PAN[21] 82.81 63.35 71.59 51.51 36.29 34.00
    PAN+SE+DyReLU+GC 83.43 64.59 72.81 53.62 37.19 35.52
    SCPNet 90.23 76.85 81.60 65.11 58.50 56.34
    SCPNet+SE+DyReLU+GC 91.09 78.31 83.39 68.03 59.86 58.58
    下载: 导出CSV
  • [1] YE M, SHEN J, LIN G, et al. Deep learning for person re-identification: A survey and outlook[J/OL]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021(2021-01-26)[2021-02-01]. https://ieeexplore.ieee.org/document/9336268.
    [2] ZHENG Z, ZHENG L, YANG Y. A discriminatively learned CNN embedding for person reidentification[J]. ACM Transactions on Multimedia Computing, Communications, and Applications, 2017, 14(1): 1-20.
    [3] WANG F, ZUO W, LIN L, et al. Joint learning of single-image and cross-image representations for person re-identification[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2016: 1288-1296.
    [4] SUN Y, ZHENG L, YANG Y, et al. Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline)[C]//European Conference on Computer Vision. Berlin: Springer, 2018: 480-496.
    [5] ZHAO H, TIAN M, SUN S, et al. Spindle Net: Person re-identification with human body region guided feature decomposition and fusion[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2017: 1077-1085.
    [6] MIAO J, WU Y, LIU P, et al. Pose-guided feature alignment for occluded person re-identification[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2019: 542-551.
    [7] LIN Y, ZHENG L, ZHENG Z, et al. Improving person re-identification by attribute and identity learning[J]. Pattern Recognition, 2019, 95: 151-161. doi: 10.1016/j.patcog.2019.06.006
    [8] FAN X, LUO H, ZHANG X, et al. SCPNet: Spatial-channel parallelism network for joint holistic and partial person re-identification[C]//Asian Conference on Computer Vision. Berlin: Springer, 2018: 19-34.
    [9] HERMANS A, BEYER L, LEIBE B. In defense of the triplet loss for person re-identification[EB/OL]. (2017-11-21)[2020-12-01]. https://arxiv.org/abs/1703.07737.
    [10] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 7132-7141.
    [11] CHEN Y, DAI X, LIU M, et al. Dynamic ReLU[C]//European Conference on Computer Vision. Berlin: Springer, 2020: 351-367.
    [12] IOFFE S, SZEGEDY C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]//International Conference on Machine Learning. New York: ACM, 2015: 448-456.
    [13] QIAO S, WANG H, LIU C, et al. Weight standardization[EB/OL]. (2020-08-09)[2020-12-01]. https://arxiv.org/abs/1903.10520.
    [14] YONG H, HUANG J, HUA X, et al. Gradient centralization: A new optimization technique for deep neural networks[C]//European Conference on Computer Vision. Berlin: Springer, 2020: 635-652.
    [15] ZHENG L, SHEN L, TIAN L, et al. Scalable person re-identification: A benchmark[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2015: 1116-1124.
    [16] ZHENG Z, ZHENG L, YANG Y. Unlabeled samples generated by GAN improve the person re-identification baseline in vitro[C]// Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2017: 3754-3762.
    [17] LI W, ZHAO R, XIAO T, et al. DeepReID: Deep filter pairing neural network for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2014: 152-159.
    [18] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2016: 770-778.
    [19] CHEN Y C, ZHENG W S, LAI J H, et al. An asymmetric distance model for cross-view feature mapping in person reidentification[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2016, 27(8): 1661-1675.
    [20] SUN Y, ZHENG L, DENG W, et al. Svdnet for pedestrian retrieval[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2017: 3800-3808.
    [21] ZHENG Z, ZHENG L, YANG Y. Pedestrian alignment network for large-scale person re-identification[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018, 29(10): 3037-3045.
    [22] SU C, LI J, ZHANG S, et al. Pose-driven deep convolutional model for person re-identification[C]//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE Press, 2017: 3960-3969.
    [23] XU J, ZHAO R, ZHU F, et al. Attention-aware compositional network for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 2119-2128.
    [24] WEI L, ZHANG S, YAO H, et al. GLAD: Global-local-alignment descriptor for pedestrian retrieval[C]//Proceedings of the 25th ACM International Conference on Multimedia. New York: ACM, 2017: 420-428.
    [25] LI W, ZHU X, GONG S. Harmonious attention network for person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 2285-2294.
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出版历程
  • 收稿日期:  2020-12-08
  • 录用日期:  2021-02-06
  • 网络出版日期:  2022-05-20

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