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外观动作自适应目标跟踪方法

熊珺瑶 王蓉 孙义博

熊珺瑶, 王蓉, 孙义博等 . 外观动作自适应目标跟踪方法[J]. 北京航空航天大学学报, 2022, 48(8): 1525-1533. doi: 10.13700/j.bh.1001-5965.2021.0597
引用本文: 熊珺瑶, 王蓉, 孙义博等 . 外观动作自适应目标跟踪方法[J]. 北京航空航天大学学报, 2022, 48(8): 1525-1533. doi: 10.13700/j.bh.1001-5965.2021.0597
XIONG Junyao, WANG Rong, SUN Yiboet al. Appearance and action adaptive target tracking method[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1525-1533. doi: 10.13700/j.bh.1001-5965.2021.0597(in Chinese)
Citation: XIONG Junyao, WANG Rong, SUN Yiboet al. Appearance and action adaptive target tracking method[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(8): 1525-1533. doi: 10.13700/j.bh.1001-5965.2021.0597(in Chinese)

外观动作自适应目标跟踪方法

doi: 10.13700/j.bh.1001-5965.2021.0597
基金项目: 

国家自然科学基金 62076246

详细信息
    通讯作者:

    王蓉, E-mail: dbdxwangrong@163.com

  • 中图分类号: TP183

Appearance and action adaptive target tracking method

Funds: 

National Natural Science Foundation of China 62076246

More Information
  • 摘要:

    为降低目标运动时产生的外观形变对目标跟踪的影响,在DaSiamese-RPN基础上进行改进,提出了一种外观动作自适应的目标跟踪方法。在孪生网络的子网络中引入外观动作自适应更新模块,融合目标的时空信息和动作特征;利用2种欧氏距离分别度量真实图和预测图之间的全局和局部差异,并对二者加权融合构建损失函数,加强预测目标特征图与真实目标特征图之间全局和局部信息的关联性。在VOT2016、VOT2018、VOT2019和OTB100数据集上进行测试,实验结果表明:在VOT2016和VOT2018数据集上,预测平均重叠率分别提高4.5%和6.1%;在VOT2019数据集上,准确度提高0.4%,预测平均重叠率降低1%;在OTB100数据集上,跟踪成功率提高0.3%,精确度提高0.2%。

     

  • 图 1  DaSiamese-RPN整体框架

    Figure 1.  Overall framework of DaSiamese-RPN

    图 2  DaSiamese-RPN跟踪框架

    Figure 2.  Tracking framework of DaSiamese-RPN

    图 3  外观动作自适应目标跟踪框架

    Figure 3.  Framework of appearance and action adaptive target tracking

    图 4  外观动作自适应更新模块框架

    Figure 4.  Framework of appearance and action adaptive module

    图 5  Action-Net框架

    Figure 5.  Framework of Action-Net

    图 6  Action-Net各部分结构

    Figure 6.  Action-Net structure of each part

    图 7  全局局部信息联合损失框架

    Figure 7.  Framework of global and local information combination loss function

    图 8  遮挡场景下可视化

    Figure 8.  Visualization of occlusion scene

    图 9  动作复杂变化下可视化

    Figure 9.  Visualization under complex changes of movement

    表  1  VOT2016数据集测试结果

    Table  1.   Results of testing on VOT2016 dataset

    模型 准确度/% 鲁棒性/% 预测平均重叠率/%
    DaSiamese-RPN 61 22 41.1
    本文(ω=0) 62.7 21.4 42.5
    本文(ω=1 000) 61.3 19.6 45.5
    本文(ω=500) 60.9 19.6 44.2
    本文(ω=100) 61.4 18.6 45.6
    下载: 导出CSV

    表  2  VOT2018数据集测试结果

    Table  2.   Results of testing on VOT2018 dataset

    模型 准确度/% 鲁棒性/% 预测平均重叠率/%
    DaSiamese-RPN 56.9 33.7 32.6
    本文(ω=0) 58.4 29.5 35.2
    本文(ω=1 000) 58.5 28.6 37.2
    本文(ω=500) 58.5 25.8 38.7
    本文(ω=100) 58.5 28.6 36.5
    下载: 导出CSV

    表  3  VOT2019数据集测试结果

    Table  3.   Results of testing on VOT2019 dataset

    模型 准确度/% 鲁棒性/% 预测平均重叠率/%
    DaSiamese-RPN 58.2 52.7 27.2
    本文(ω=0) 58.3 54.7 26.7
    本文(ω=1 000) 58.5 55.2 26.8
    本文(ω=500) 58.6 55.2 26.2
    本文(ω=100) 58.5 55.7 26
    下载: 导出CSV

    表  4  OTB100数据集测试结果

    Table  4.   Results of testing on OTB100 dataset

    模型 跟踪成功率/% 精确度/%
    DaSiamese-RPN 64.6 85.9
    本文(ω=0) 64.9 86.1
    本文(ω=1 000) 64.5 85.5
    本文(ω=500) 64.6 85.8
    本文(ω=100) 64.8 86
    下载: 导出CSV

    表  5  不同方法在VOT2016数据集上的对比

    Table  5.   Comparison with different methods on VOT2016 dataset

    方法 预测平均重叠率/% 准确度/% 鲁棒性/%
    MemTrack[19] 27.3 53.3 144.1
    SiamFC[11] 23.5 52.9 190.8
    SiamRPN[12] 26.2 53.8 42.4
    SiamRPNpp[2] 39.3 61.8 23.8
    本文 45.6 61.4 18.6
    下载: 导出CSV

    表  6  不同方法在VOT2018数据集上的对比

    Table  6.   Comparison with different methods on VOT2018 dataset

    方法 预测平均重叠率/% 准确度/% 鲁棒性/%
    DRT[20] 35.5 51.8 20.1
    RCO[16] 37.6 50.7 15.5
    UPDT[21] 37.9 53.6 18.4
    SiamRPN[12] 38.4 50.5 14
    MFT[16] 38.6 50.3 15.9
    LADCF[22] 38.9 50.3 15.9
    SiamRPNpp[2] 35.2 57.6 27
    本文 38.7 58.5 25.8
    下载: 导出CSV

    表  7  不同方法在OTB100数据集上的对比

    Table  7.   Comparison with different methods on OTB100 dataset

    方法 跟踪成功率/% 精确度/%
    SiamFC[11] 58.9 79.4
    GradNet[23] 63.9 86.1
    C-RPN[24] 63.9 85.2
    SiamRPN[12] 63.7 85.1
    SiamRPNpp[2] 64.8 85.3
    FENG[25] 61 73
    SNLT[26] 67 80
    本文 64.9 86.1
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-10-09
  • 录用日期:  2021-10-29
  • 刊出日期:  2021-11-16

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