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基于背景抑制颜色分布新模型的合成式目标跟踪算法

陈昭炯 叶东毅 林德威

陈昭炯, 叶东毅, 林德威.基于背景抑制颜色分布新模型的合成式目标跟踪算法.自动化学报, 2021, 47(3): 630-640 doi: 10.16383/j.aas.c180147
引用本文: 陈昭炯, 叶东毅, 林德威.基于背景抑制颜色分布新模型的合成式目标跟踪算法.自动化学报, 2021, 47(3): 630-640 doi: 10.16383/j.aas.c180147
CHEN Zhao-Jiong, YE Dong-Yi, LIN De-Wei. A Synthetic Target Tracking Algorithm Based on a New Color Distribution Model With Background Suppression. Acta Automatica Sinica, 2021, 47(3): 630-640 doi: 10.16383/j.aas.c180147
Citation: CHEN Zhao-Jiong, YE Dong-Yi, LIN De-Wei. A Synthetic Target Tracking Algorithm Based on a New Color Distribution Model With Background Suppression. Acta Automatica Sinica, 2021, 47(3): 630-640 doi: 10.16383/j.aas.c180147

基于背景抑制颜色分布新模型的合成式目标跟踪算法

doi: 10.16383/j.aas.c180147
基金项目: 

国家自然科学基金 61672158

福建省自然科学基金 2018J1798

详细信息
    作者简介:

    陈昭炯  福州大学数学与计算机科学学院教授. 主要研究方向为图像处理. E-mail: chenzj@fzu.edu.cn

    林德威  福州大学数学与计算机科学学院硕士研究生. 主要研究方向为图像处理. E-mail: ifltrain@163.com

    通讯作者:

    叶东毅  福州大学数学与计算机科学学院教授. 主要研究方向为机器学习, 图像处理. 本文通信作者. E-mail: yiedy@fzu.edu.cn

  • 本文责任编委 杨健

A Synthetic Target Tracking Algorithm Based on a New Color Distribution Model With Background Suppression

Funds: 

National Natural Science Foundation of China 61672158

Natural Science Grant of Fujian Province 2018J1798

More Information
    Author Bio:

    CHEN Zhao-Jiong  Professor at the College of Mathematics and Computer Science, Fuzhou University. Her main research interest is image processing

    LIN De-Wei  Master student at the College of Mathematics and Computer Science, Fuzhou University. His main research interest is image processing

    Corresponding author: YE Dong-Yi  Professor at the College of Mathematics and Computer Science, Fuzhou University. His research interest covers machine learning and image processing. Corresponding author of this paper
  • Recommended by Associate Editor YANG Jian
  • 摘要: 传统的基于直方图分布的目标颜色模型, 由于跟踪过程的实时性要求其区间划分不宜过细, 因此易导致同一区间有差异的颜色难以区分; 此外, 还存在易受背景干扰的问题. 本文提出一种新的背景抑制目标颜色分布模型, 并在此基础上设计了一个合成式的目标跟踪算法. 新的颜色分布模型将一阶及二阶统计信息纳入模型, 并设计了基于人类视觉特性的权重计算方式, 能有效区分同一区间内的差异色且抑制背景颜色在模型中的比重; 算法基于该颜色模型构建目标的产生式模型, 并引入结合方向梯度直方图(Histogram of oriented gradient, HOG) 特征的相关滤波器对目标形状进行判别式建模, 同时将两个模型相互融合; 针对融合参数不易设计的难点, 分析并建立了一套定性原则, 用于判定模型各自的可信度并指导模型更新; 最终利用粒子群算法的搜索机制对候选目标的位置、尺度进行搜索, 其中适应值函数设计为两个跟踪器的融合结果. 实验结果表明, 本文算法在绝大多数情况下准确率较对比算法更优且能满足实时性要求.
    Recommended by Associate Editor YANG Jian
    1)  本文责任编委 杨健
  • 图  1  同一区间内的相近色

    Fig.  1  Similar colors within the same interval

    图  2  目标框与实际目标形状差异

    Fig.  2  Shape difference between the tracking box and the real object

    图  3  与目标紧邻的参考背景模型

    Fig.  3  Reference model of background close to the target

    图  4  粒子模型示意图

    Fig.  4  Illustration of particle model

    图  5  颜色与形状跟踪器权衡选择过程图示

    Fig.  5  Trade-off between color tracker and shape tracker

    图  6  本文算法过程示意图

    Fig.  6  Illustration of the proposed algorithm

    图  7  3个算法OPE跟踪准确率和成功率图

    Fig.  7  OPE tracking accuracy rate and success rate of three algorithms

    图  8  BlurOwl图像序列3个算法跟踪截图

    Fig.  8  Screen shots of tracking with three algorithms on BlurOwl image sequences

    图  9  Girl2图像序列3个算法跟踪截图

    Fig.  9  Screen shots of tracking with three algorithms on Girl2 image sequences

    图  10  Human5图像序列3个算法跟踪截图

    Fig.  10  Screen shots of tracking with three algorithms on Human5 image sequences

    图  11  Skating1图像序列3个算法跟踪截图

    Fig.  11  Screen shots of tracking with three algorithms on Skating1 image sequences

    图  12  Diving图像序列3个算法跟踪截图

    Fig.  12  Screen shots of tracking with three algorithms on Diving image sequences

    表  1  3个算法的总体性能平均值

    Table  1  Average global performance of three algorithms

    算法 CLE平均值 OS平均值 平均帧率(帧/s)
    本文 14.82 0.6616 33.82
    Staple 30 0.5108 26.42
    KCF 59.67 0.4626 121.82
    下载: 导出CSV

    表  2  3个算法在18个视频的CLE值比较

    Table  2  CLE values of three algorithms on 18 videos

    序列名 序列特点 本文 Staple KCF
    BlurFace 1, 2 3 4 6
    BlurOwl 1, 2, 3 9 62 70
    Butterfly 4 35 39 130
    Couple 2, 3 9 23 43
    Diving 3, 4 35 83 136
    DragonBaby 1 13 22 24
    Football 6 6 10 9
    Girl2 3, 4, 6 8 77 11
    Human2 3, 5, 6 15 18 68
    Human4 4, 5, 6 7 9 71
    Human5 1, 3 8 30 132
    Human6 1, 4, 6 7 7 20
    Iceskater1 3, 4 38 93 40
    Jogging 6 5 45 7
    Jumping 1, 2 6 12 10
    Singer1 3, 5 7 10 10
    Skating1 3, 4, 5 18 47 13
    Skating2 3, 4 30 33 37
    下载: 导出CSV

    表  3  3个算法在18个视频的OS指标比较

    Table  3  OS values of three algorithms on 18 videos

    序列名 本文 Staple KCF
    BlurFace 0.9029 0.8491 0.8567
    BlurOwl 0.8082 0.4263 0.1458
    Butterfly 0.4284 0.4040 0.1251
    Couple 0.6693 0.5306 0.0674
    Diving 0.3006 0.2445 0
    DragonBaby 0.5863 0.5019 0.4336
    Football 0.7317 0.5825 0.6068
    Girl2 0.7515 0.1100 0.7002
    Human2 0.7896 0.7322 0.6035
    Human4 0.6606 0.6661 0.3389
    Human5 0.7243 0.4862 0.1808
    Human6 0.7835 0.8054 0.5969
    Iceskater1 0.4493 0.1979 0.4054
    Jogging 0.7373 0.1747 0.6131
    Jumping 0.6841 0.2468 0.4596
    Singer1 0.8253 0.6952 0.8169
    Skating1 0.6910 0.4105 0.7030
    Skating2 0.5231 0.4797 0.3890
    下载: 导出CSV
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  • 收稿日期:  2018-03-15
  • 录用日期:  2019-01-09
  • 刊出日期:  2021-04-02

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