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基于广义关联聚类图的分层关联多目标跟踪

齐美彬 岳周龙 疏坤 蒋建国

齐美彬, 岳周龙, 疏坤, 蒋建国. 基于广义关联聚类图的分层关联多目标跟踪. 自动化学报, 2017, 43(1): 152-160. doi: 10.16383/j.aas.2017.c150519
引用本文: 齐美彬, 岳周龙, 疏坤, 蒋建国. 基于广义关联聚类图的分层关联多目标跟踪. 自动化学报, 2017, 43(1): 152-160. doi: 10.16383/j.aas.2017.c150519
QI Mei-Bin, YUE Zhou-Long, SHU Kun, JIANG Jian-Guo. Multi-object Tracking Using Hierarchical Data Association Based on Generalized Correlation Clustering Graphs. ACTA AUTOMATICA SINICA, 2017, 43(1): 152-160. doi: 10.16383/j.aas.2017.c150519
Citation: QI Mei-Bin, YUE Zhou-Long, SHU Kun, JIANG Jian-Guo. Multi-object Tracking Using Hierarchical Data Association Based on Generalized Correlation Clustering Graphs. ACTA AUTOMATICA SINICA, 2017, 43(1): 152-160. doi: 10.16383/j.aas.2017.c150519

基于广义关联聚类图的分层关联多目标跟踪

doi: 10.16383/j.aas.2017.c150519
基金项目: 

安徽省科技攻关项目 1301b042023

国家自然科学基金 61371155

详细信息
    作者简介:

    齐美彬合肥工业大学计算机与信息学院教授.主要研究方向为视频编码, 运动目标检测与跟踪, DSP技术.E-mail:qimeibin@163.com

    疏坤合肥工业大学计算机与信息学院硕士研究生.主要研究方向为计算机视觉和图像检索.E-mail:shudaxia123@163.com

    蒋建国合肥工业大学计算机与信息学院教授.主要研究方向为数字图像分析与处理, 分布式智能系统, DSP技术及应用.E-mail:jgjiang@hfut.edu.cn

    通讯作者:

    岳周龙合肥工业大学计算机与信息学院硕士研究生.主要研究方向为计算机视觉, 图像处理, 多目标跟踪.本文通信作者.E-mail:yuezl_2012@163.com

Multi-object Tracking Using Hierarchical Data Association Based on Generalized Correlation Clustering Graphs

Funds: 

Science and Technology Brainstorm Project of An- hui Province 1301b042023

National Natural Science Foundation of China 61371155

More Information
    Author Bio:

    Professor at the School of Computer and Information, Hefei University of Technology. His research interest covers video coding, moving target detection and tracking, and DSP technol-ogy.

    Master student at the School of Computer and Information, Hefei University of Technology. His re-search interest covers computer vision and image retrieval.

    Professor at the School of Computer and Information, Hefei University of Technology. His research interest covers digital image analysis and processing, distributed intelligent systems, DSP technology and applications.

    Corresponding author: YUE Zhou-Long Master student at the School of Computer and Infor-mation, Hefei University of Technology. His research interest covers computer vision, image processing, and multi-object tracking. Cor-responding author of this paper.
  • 摘要: 检测跟踪是近期多目标跟踪研究的热点方向之一.目前大部分方法都是基于相邻帧之间的双向匹配,对检测点进行数据融合.本文提出的方法是,给定一个滑动时间窗口,在窗口内对某个目标每帧出现的检测点进行一次性数据融合.我们把多目标跟踪看作图的分割问题,利用广义关联聚类(Generalized correlation clustering problem,GCCP)图优化文中提出的数据融合.吸取分层数据关联的思想,把多目标跟踪分成两个阶段.首先,在时间窗口内遵循检测点,利用广义关联聚类,得到自适应长度的轨迹片段,轨迹片段长度不受窗口宽度的限制.然后,基于轨迹片段进一步数据关联,得到目标的长轨迹.在公共数据集上的实验测试表明,本文方法能够有效地实现多目标跟踪,对于遮挡处理、身份转换处理以及轨迹的生成具有很好的鲁棒性,多目标跟踪准确率(Multiple object tracking accuracy,MOTA)超过当前水平.
  • 图  1  GCCP的模型

    Fig.  1  GCCP model

    图  2  速度v 的估计

    Fig.  2  Estimated v

    图  3  多目标跟踪过程

    Fig.  3  The steps of multi-object tracking

    图  4  不同的轨迹片段

    Fig.  4  Different tracklets

    图  5  估计轨迹片段速度

    Fig.  5  Estimate speed of tracklet

    图  6  运动轨迹展示图

    Fig.  6  Trajectory of multi-object

    表  1  自适应轨迹片段与传统轨迹片段比较

    Table  1  The difference between traditional-tracklet and adaptive-tracklet

    Methods MOTA(%) MOTP(%) IDSTC Time(s) Rcll(%) Prcsn(%)
    Traditional-tracklet 93.0883.57163562.594.6198.74
    Adaptive-tracklet 95.1183.04477 2.34 96.3198.88
    下载: 导出CSV

    表  2  跟踪结果

    Table  2  The results of tracking

    Sequence MethodsMOTA(%) MOTP(%) Rcll(%) Prcsn(%)
    Longyin[13] 92.772.994.498.4
    Andriyenko[15] 88.379.690 98.7
    PETS-S2L1 Zamir[10] 90.369.0296.45 93.64
    Afshin[18] 90.463.12 -- --
    Izadinia[12] 90.77695.296.8
    Ours
    95.1183.0496.3198.88
    Longyin[13] 62.152.771.290.3
    PETS-S2L2 Anton[26] 56.959.465.589.8
    Ours
    52.9868.6357.1593.58
    Longyin[13] 88.481.990.898.3
    Andriyenko[15] 73.176.586.889.4
    Parking lot Zamir[10] 90.4374.185.398.2
    Guang[8] 79.374.1 -- --
    Izadinia[12] 88.977.596.593.6
    Ours
    91.3774.896.6195.24
    Zamir[10] 75.5971.93 -- --
    Guang[8] 72.971.3 -- --
    Town center McLaughlin[6] 74.1572.4183.2790.4
    Izadinia[12] 75.771.681.893.6
    Ours79.6770.684.5693.98
    下载: 导出CSV
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出版历程
  • 收稿日期:  2015-08-13
  • 录用日期:  2016-04-08
  • 刊出日期:  2017-01-01

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