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具有尺度和旋转适应性的长时间目标跟踪

熊丹 卢惠民 肖军浩 郑志强

熊丹, 卢惠民, 肖军浩, 郑志强. 具有尺度和旋转适应性的长时间目标跟踪. 自动化学报, 2019, 45(2): 289-304. doi: 10.16383/j.aas.2018.c170359
引用本文: 熊丹, 卢惠民, 肖军浩, 郑志强. 具有尺度和旋转适应性的长时间目标跟踪. 自动化学报, 2019, 45(2): 289-304. doi: 10.16383/j.aas.2018.c170359
XIONG Dan, LU Hui-Min, XIAO Jun-Hao, ZHENG Zhi-Qiang. Robust Long-term Object Tracking With Adaptive Scale and Rotation Estimation. ACTA AUTOMATICA SINICA, 2019, 45(2): 289-304. doi: 10.16383/j.aas.2018.c170359
Citation: XIONG Dan, LU Hui-Min, XIAO Jun-Hao, ZHENG Zhi-Qiang. Robust Long-term Object Tracking With Adaptive Scale and Rotation Estimation. ACTA AUTOMATICA SINICA, 2019, 45(2): 289-304. doi: 10.16383/j.aas.2018.c170359

具有尺度和旋转适应性的长时间目标跟踪

doi: 10.16383/j.aas.2018.c170359
基金项目: 

国家自然科学基金 61503401

国家自然科学基金 61403409

中国博士后基金 2014M562648

详细信息
    作者简介:

    熊丹  国防科技大学智能科学学院博士研究生.2012年获得国防科技大学硕士学位.主要研究方向为机器人视觉和视觉SLAM.E-mail:xiongdan@nudt.edu.cn

    肖军浩  国防科技大学智能科学学院讲师.2007年获得国防科技大学学士学位, 2013年获得德国汉堡大学博士学位.主要研究方向为移动机器人三维感知和多机器人协同控制.E-mail:junhao.xiao@ieee.org

    郑志强  国防科技大学智能科学学院教授.1994年获得比利时列日大学博士学位.主要研究方向为多机器人协同控制, 飞行器控制.E-mail:zqzheng@nudt.edu.cn

    通讯作者:

    卢惠民  国防科技大学智能科学学院副教授.2010年获得国防科技大学博士学位.主要研究方向为机器人视觉, 视觉SLAM和机器人足球.本文通信作者.E-mail:lhmnew@nudt.edu.cn

Robust Long-term Object Tracking With Adaptive Scale and Rotation Estimation

Funds: 

National Natural Science Foundation of China 61503401

National Natural Science Foundation of China 61403409

China Postdoctoral Science Foundation 2014M562648

More Information
    Author Bio:

     Ph. D. candidate at the College of Intelligence Science and Technology, National University of Defense Technology. He received his master degree from National University of Defense Technology in 2012. His research interest covers robot vision and visual SLAM

     Lecturer at the College of Intelligence Science and Technology, National University of Defense Technology. He received his bachelor degree from National University of Defense Technology in 2007, and Ph. D. degree from University of Hamburg, Germany in 2013. His research interest covers 3D perception for mobile robots and multi-robot coordination

     Professor at the College of Intelligence Science and Technology, National University of Defense Technology. He received his Ph. D. degree from University of Liege, Belgium in 1994. His research interest covers multi-robot coordination control and flight control

    Corresponding author: LU Hui-Min  Associate professor at the College of Intelligence Science and Technology, National University of Defense Technology. He received his Ph. D. degree from National University of Defense Technology in 2010. His research interest covers robot vision, visual SLAM and robot soccer. Corresponding author of this paper
  • 摘要: 目标发生尺度和旋转变化会给长时间目标跟踪带来很大的挑战,针对该问题,本文提出了具有尺度和旋转适应性的鲁棒目标跟踪算法.首先针对跟踪过程中目标存在的尺度变化和旋转运动,提出一种基于傅里叶-梅林变换和核相关滤波的目标尺度和旋转参数估计方法.该方法能够实现连续空间的目标尺度和旋转参数估计,采用核相关滤波提高了估计的鲁棒性和准确性.然后针对长时间目标跟踪过程中,有时不可避免地会出现跟踪失败的情况(例如由于长时间半遮挡或全遮挡等),提出一种基于直方图和方差加权的目标搜索方法.当目标丢失时,通过提出的搜索方法能够快速从图像中确定目标可能存在的区域,使得跟踪算法具有从失败中恢复的能力.本文还训练了两个核相关滤波器用于估计跟踪结果的置信度和目标平移,通过专门的核相关滤波器能够使得估计的跟踪结果置信度更加准确和鲁棒,置信度的估计结果可用于激活基于直方图和方差加权的目标搜索模块,并判断搜索窗口中是否包含目标.本文在目标跟踪标准数据集(Online object tracking benchmark,OTB)上对提出的算法和目前主流的目标跟踪算法进行对比实验,验证了本文提出算法的有效性和优越性.
    1)  本文责任编委 左旺孟
  • 图  1  基于傅里叶-梅林变换的核相关滤波模型学习

    Fig.  1  The kernelized correlation filtering model learning based on the Fourier-Mellin transform

    图  2  用于目标位移估计和跟踪结果置信度估计的核相关滤波模型

    Fig.  2  The kernelized correlation filtering models for the estimation of object translation and the confidence of the tracking result

    图  3  具有尺度和旋转适应性的长时间目标跟踪算法框图

    Fig.  3  The architecture of robust long-term object tracking with adaptive scale and rotation estimation

    图  4  目标标注真值、跟踪产生的非标准矩形和非标准矩形的外接矩形示意图

    Fig.  4  The diagram of target annotations, nonstandard rectangles from our trackers and external rectangles of nonstandard rectangles

    图  5  通过OPE, SRE和TRE估算准则得到的跟踪算法精度图和成功率图

    Fig.  5  Precision plots and success rate plots of tracking algorithms evaluated by OPE, SRE and TRE standards

    图  6  不同视觉挑战情况下通过OPE估算准则得到的跟踪算法精度图和成功率图

    Fig.  6  Precision plots and success rate plots of tracking algorithms evaluated by OPE standard under different visual tracking challenges

    图  7  RLOT, ROT, SRDCF[19], LCT[23], TLD[14]和Struck[28]在11个OTB序列上的跟踪结果

    Fig.  7  Tracking results using RLOT, ROT, SRDCF[19], LCT[23], TLD[14] and Struck[28] on 11 OTB image sequences

    表  1  三个核相关滤波器参数

    Table  1  The parameters of three kernelized

    核相关滤波器位移估计 置信度估计 尺度旋转估计
    核相关滤波器 核相关滤波器 核相关滤波器
    高斯核宽度参数 $\sigma $ 0.6 0.6 0.4
    学习率 $\beta$ 0.012 0.012 0.075
    高斯标签宽度参数 $s$ 0.125 $\sqrt{mn}$ 0.125 $\sqrt{mn}$ 0.075( $d$ , $a$ )
    正则化参数 $\alpha$ $10^{-4} $ $10^{-4} $ $5^{-5} $
    下载: 导出CSV

    表  2  OTB数据集中选择的11个序列包含的视觉挑战

    Table  2  The visual tracking challenges included in the 11 image sequences selected from the OTB datasets

    图像序列 光照变化 平面外旋转 尺变变化 半遮挡 扭曲 运动模糊 快速运动 平面内旋转 出相机视野 凌乱背景 低分辨率
    David 1 1 1 1 1 1 0 1 0 0 0
    CarScale 0 1 1 1 0 0 1 1 0 0 0
    Dog1 0 1 1 0 0 0 0 1 0 0 0
    FaceOcc2 1 1 0 1 0 0 0 1 0 0 0
    Jogging-2 0 1 0 1 1 0 0 0 0 0 0
    Lemming 1 1 1 1 0 0 1 0 1 0 0
    MotorRolling 1 0 1 0 0 1 1 1 0 1 1
    Shaking 1 1 1 1 1 0 0 0 0 1 0
    Singer2 1 1 0 0 1 0 0 1 0 1 0
    Tiger1 1 1 0 1 1 1 1 1 0 0 0
    Soccer 1 1 1 1 0 1 1 1 0 1 0
    下载: 导出CSV

    表  3  不同跟踪算法的平均处理帧速率

    Table  3  The average frame rates of different object tracking algorithms

    跟踪算法 帧速率
    RLOT 36
    SRDCF 4
    LCT 27.4
    KCF 167
    DSST 27
    TLD 20
    Struck 28
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-06-29
  • 录用日期:  2017-11-17
  • 刊出日期:  2019-02-20

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