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基于深度学习的多目标跟踪关联模型设计

侯建华 张国帅 项俊

侯建华, 张国帅, 项俊.基于深度学习的多目标跟踪关联模型设计.自动化学报, 2020, 46(12): 2690−2700 doi: 10.16383/j.aas.c180528
引用本文: 侯建华, 张国帅, 项俊.基于深度学习的多目标跟踪关联模型设计.自动化学报, 2020, 46(12): 2690−2700 doi: 10.16383/j.aas.c180528
Hou Jian-Hua, Zhang Guo-Shuai, Xiang Jun. Designing affinity model for multiple object tracking based on deep learning. Acta Automatica Sinica, 2020, 46(12): 2690−2700 doi: 10.16383/j.aas.c180528
Citation: Hou Jian-Hua, Zhang Guo-Shuai, Xiang Jun. Designing affinity model for multiple object tracking based on deep learning. Acta Automatica Sinica, 2020, 46(12): 2690−2700 doi: 10.16383/j.aas.c180528

基于深度学习的多目标跟踪关联模型设计

doi: 10.16383/j.aas.c180528
基金项目: 

国家自然科学基金 61671484

国家自然科学基金 61701548

湖北省自然科学基金 2018CFB503

中南民族大学中央高校基本科研业务费专项资金项目 CZQ17001

中南民族大学中央高校基本科研业务费专项资金项目 CZZ18001

中南民族大学中央高校基本科研业务费专项资金项目 CZY18046

详细信息
    作者简介:

    侯建华 中南民族大学电子信息工程学院教授. 2007年获华中科技大学模式识别与智能系统博士学位.主要研究方向为计算机视觉与模式识别. E-mail: zil@scuec.edu.cn

    张国帅 中南民族大学电子信息工程学院硕士研究生. 2016年获长春大学学士学位.主要研究方向为图像处理与模式识别. E-mail: guoshuaiz@scuec.edu.cn

    通讯作者:

    项俊 中南民族大学电子信息工程学院讲师. 2016年获华中科技大学控制科学与工程博士学位.主要研究方向为计算机视觉与模式识别.本文通信作者. E-mail: junxiang@scuec.edu.cn

  • 本文责任编委  桑农

Designing Affinity Model for Multiple Object Tracking Based on Deep Learning

Funds: 

National Natural Science Foundation of China 61671484

National Natural Science Foundation of China 61701548

Hubei Provincial Natural Science Foundation of China 2018CFB503

Fundamental Research Funds for the Central Universities, South-Central University for Nationalities CZQ17001

Fundamental Research Funds for the Central Universities, South-Central University for Nationalities CZZ18001

Fundamental Research Funds for the Central Universities, South-Central University for Nationalities CZY18046

More Information
    Author Bio:

    HOU Jian-Hua  Professor at the College of Electronic Information Engineering, South-Central University for Nationalities. He received his Ph. D. degree in pattern recognition and intelligent system from Huazhong University of Science and Technology in 2007. His research interest covers computer vision and pattern recognition

    ZHANG Guo-Shuai  Master student at the College of Electronic Information Engineering, South-Central University for Nationalities. He received his bachelor degree from Changchun University in 2016. His research interest covers image processing and pattern recognition

    Corresponding author: XIANG Jun  Lecturer at the College of Electronic Information Engineering, South-Central University for Nationalities. She received her Ph. D. degree in control science and engineering from Huazhong University of Science and Technology in 2016. Her research interest covers computer vision and pattern recognition. Corresponding author of this paper
  • Recommended by Associate Editor SANG Nong
  • 摘要: 近年来, 深度学习在计算机视觉领域的应用取得了突破性进展, 但基于深度学习的视频多目标跟踪(Multiple object tracking, MOT)研究却相对甚少, 而鲁棒的关联模型设计是基于检测的多目标跟踪方法的核心.本文提出一种基于深度神经网络和度量学习的关联模型:采用行人再识别(Person re-identification, Re-ID)领域中广泛使用的度量学习技术和卷积神经网络(Convolutional neural networks, CNNs)设计目标外观模型, 即利用三元组损失函数设计一个三通道卷积神经网络, 提取更具判别性的外观特征构建目标外观相似度; 再结合运动模型计算轨迹片间的关联概率.在关联策略上, 采用匈牙利算法, 首先以逐帧关联方式得到短小可靠的轨迹片集合, 再通过自适应时间滑动窗机制多级关联, 输出各目标最终轨迹.在2DMOT2015、MOT16公开数据集上的实验结果证明了所提方法的有效性, 与当前一些主流算法相比较, 本文方法取得了相当或者领先的跟踪效果.
    Recommended by Associate Editor SANG Nong
    1)  本文责任编委  桑农
  • 图  1  多目标跟踪方法整体框架

    Fig.  1  The overall framework of multi-object tracking method

    图  2  三通道外观模型训练框图

    Fig.  2  Three-channel appearance model training block diagram

    图  3  自适应时间滑动窗原理示意图

    Fig.  3  Diagram of adaptive time sliding window principle

    图  4  多级关联中的运动模型示意图

    Fig.  4  Diagram of motion model in multi-level association

    图  5  MOT16-01跟踪结果(从左到右依次为第121、174、248帧)

    Fig.  5  Tracking results of MOT16-01 (121st, 174th, 248th frames from left to right)

    图  6  MOT16-03跟踪结果(从左到右依次为第249、307、424帧)

    Fig.  6  Tracking results of MOT16-03 (249th, 307th, 424th frames from left to right)

    图  7  MOT16-07跟踪结果(从左到右依次为第397、455、500帧)

    Fig.  7  Tracking results of MOT16-07 (397th, 455th, 500th frames from left to right)

    图  8  MOT16-06跟踪结果(从左到右依次为第537、806、1188帧)

    Fig.  8  Tracking results of MOT16-06 (537th, 806th, 1188th frames from left to right)

    图  9  MVI_20032跟踪结果(从左到右依次为第332、360、423帧)

    Fig.  9  Tracking results of MVI_20032 (332nd, 360th, 423rd frames from left to right)

    图  10  MVI_39771跟踪结果(从左到右依次为第1、54、113帧)

    Fig.  10  Tracking results of MVI_39771 (1st, 54th, 113th frames from left to right)

    表  1  剥离对比实验结果

    Table  1  Results of ablation study

    Trackers MOTA ($\uparrow$) MOTP ($\uparrow$) MT ($\uparrow$) (%) ML ($\downarrow$) (%) FP ($\downarrow$) FN ($\downarrow$) IDS ($\downarrow$)
    A + T 19.5 74.6 7.41 66.70 109 14 202 43
    M + T 17.6 74.6 7.40 64.80 307 14 326 70
    A + M + T 21.0 74.3 9.26 70.40 175 13 893 16
    A + M + V 14.7 75.1 1.85 67.00 60 14 804 339
    下载: 导出CSV

    表  2  MOT16测试集结果

    Table  2  Results of MOT16 test set

    Trackers Mode MOTA ($\uparrow$) MOTP ($\uparrow$) MT ($\uparrow$) (%) ML ($\downarrow$) (%) FP ($\downarrow$) FN ($\downarrow$) IDS ($\downarrow$) HZ ($\uparrow$)
    AMIR[20] Online 47.2 75.8 14.0 41.6 2 681 92 856 774 1.0
    CDA[45] Online 43.9 74.7 10.7 44.4 6 450 95 175 676 0.5
    本文 Online 43.1 74.2 12.4 47.7 4 228 99 057 495 0.7
    EAMTT[41] Online 38.8 75.1 7.9 49.1 8 114 102 452 965 11.8
    OVBT[42] Online 38.4 75.4 7.5 47.3 11 517 99 463 1 321 0.3
    [2mm] Quad-CNN[17] Batch 44.1 76.4 14.6 44.9 6 388 94 775 745 1.8
    LIN1[43] Batch 41.0 74.8 11.6 51.3 7 896 99 224 430 4.2
    CEM[44] Batch 33.2 75.8 7.8 54.4 6 837 114 322 642 0.3
    下载: 导出CSV

    表  3  2DMOT2015测试集结果

    Table  3  Results of 2DMOT2015 test set

    Trackers Mode MOTA ($\uparrow$) MOTP ($\uparrow$) MT ($\uparrow$) (%) ML ($\downarrow$) (%) FP ($\downarrow$) FN ($\downarrow$) IDS ($\downarrow$) HZ ($\uparrow$)
    AMIR[20] Online 37.6 71.7 15.8 26.8 7 933 29 397 1 026 1.9
    本文 Online 34.2 71.9 8.9 40.6 7965 31665 794 0.7
    CDA[45] Online 32.8 70.7 9.7 42.2 4 983 35 690 614 2.3
    RNN_LSTM[24] Online 19.0 71.0 5.5 45.6 11 578 36 706 1 490 165.2
    Quad-CNN[17] Batch 33.8 73.4 12.9 36.9 7 879 32 061 703 3.7
    MHT_DAM[46] Batch 32.4 71.8 16.0 43.8 9 064 32 060 435 0.7
    CNNTCM[22] Batch 29.6 71.8 11.2 44.0 7 786 34 733 712 1.7
    Siamese CNN[19] Batch 29.0 71.2 8.5 48.4 5 160 37 798 639 52.8
    LIN1[43] Batch 24.5 71.3 5.5 64.6 5 864 40 207 298 7.5
    下载: 导出CSV

    表  4  UA-DETRAC数据集跟踪结果

    Table  4  Tracking results of UA-DETRAC dataset

    MOTA ($\uparrow$) MOTP ($\uparrow$) MT ($\uparrow$) (%) ML ($\downarrow$) (%) FP ($\downarrow$) FN ($\downarrow$) IDS ($\downarrow$)
    车辆跟踪 65.3 78.5 75.0 8.3 1 069 481 27
    下载: 导出CSV
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  • 收稿日期:  2018-08-02
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