2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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

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
  • 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
  • [1] Wu Y, Lim J, Yang M H. Online object tracking: A benchmark. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA: IEEE, 2013. 2411-2418
    [2] Zhang K, Zhang L, Yang M H. Real-time compressive cracking. In: Proceedings of the 2012 European Conference on Computer Vision. Florence, Italy: Springer-Verlag, 2012. 864-877
    [3] Hare S, Golodetz S, Saffari A, Vineet V, Cheng M M, Hicks S L, Torr P H S. Struck: Structured output tracking with kernels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 23(5): 263-270 http://ieeexplore.ieee.org/document/7360205/citations
    [4] Bolme D S, Beveridge J R, Draper B A, Lui Y M. Visual object tracking using adaptive correlation filters. In: Proceedings of the 2010 IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE, 2010. 2544-2550
    [5] Henriques J F, Caseiro R, Martins P, Batista J. High-speed tracking with kernelized correlation filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 583-596 doi: 10.1109/TPAMI.2014.2345390
    [6] Bertinetto L, Valmadre J, Golodetz S, Miksik O, Torr P H S. Staple: Complementary learners for real-time tracking. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 1401-1409
    [7] 刘大千, 刘万军, 费博雯, 曲海成. 前景约束下的抗干扰匹配目标跟踪方法. 自动化学报, 2018, 44(6): 1139-1152 doi: 10.16383/j.aas.2017.c160475

    Liu Da-Qian, Liu Wan-Jun, Fei Bo-Wen, Qu Hai-Cheng. A new method of anti-interference matching under foreground constraint for target tracking. Acta Automatic Sinica, 2018, 44(6): 1139-1152 doi: 10.16383/j.aas.2017.c160475
    [8] 张焕龙, 胡士强, 杨国胜. 基于外观模型学习的视频目标跟踪方法综述. 计算机研究与发展, 2015, 52(1): 177-190 http://www.cnki.com.cn/Article/CJFDTotal-JFYZ201501019.htm
    [9] Nummiaro K, Koller-Meier E, Gool L V. An adaptive color-based particle filter. Image and Vision Computing, 2003, 21(1): 99-110 doi: 10.1016/S0262-8856(02)00129-4
    [10] Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564-575 doi: 10.1109/TPAMI.2003.1195991
    [11] Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE, 2005. 886-893
    [12] 胡扬, 张东波, 段琪. 目标鲁棒识别的抗旋转HDO局部特征描述. 自动化学报, 2017, 43(4): 665-673 doi: 10.16383/j.aas.2017.c150837

    Hu Yang, Zhang Dong-Bo, Duan Qi. An improved rotation-invariant HDO local description for object recognition. Acta Automatic Sinica, 2017, 43(4): 665-673 doi: 10.16383/j.aas.2017.c150837
    [13] Jin J, Dundar A, Bates J, Farabet C, Culurciello E. Tracking with deep neural networks. In: Proceedings of the 47th Annual Conference on Information Sciences and Systems. Baltimore, USA: IEEE, 2013. 1-5
    [14] Wang L, Liu T, Wang G, Chan K L, Yang Q X. Video tracking using learned hierarchical features. IEEE Transactions on Image Processing, 2015, 24(4): 1424-1435 doi: 10.1109/TIP.2015.2403231
    [15] 张慧, 王坤峰, 王飞跃. 深度学习在目标视觉检测中的应用进展与展望. 自动化学报, 2017, 43(8): 1289-1305 doi: 10.16383/j.aas.2017.c160822

    Zhang Hui, Wang Kun-Feng, Wang Fei-Yue. Advances and perspectives on applications of deep learning in visual object detection. Acta Automatic Sinica, 2017, 43(8): 1289-1305 doi: 10.16383/j.aas.2017.c160822
    [16] 管皓, 薛向阳, 安志勇, 深度学习在视频目标跟踪中的应用进展与展望, 自动化学报, 2016, 42(6): 834-847 doi: 10.16383/j.aas.2016.c150705

    Guan Hao, Xue Xiang-Yang, An Zhi-Yong. Advances on application of deep learning for video object tracking. Acta Automatic Sinica, 2016, 42(6): 834-847 doi: 10.16383/j.aas.2016.c150705
    [17] Sun C, Wang D, Lu H C, Yang M H. Learning spatial-aware regressions for visual tracking. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018. 8962-8970
    [18] Possegger H, Mauthner T, Bischof H. In defense of color-based model-free tracking. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE, 2015. 2113-2120
    [19] Danelljan M, Khan F S, Felsberg M, Van de Weijer J. Adaptive color attributes for real-time visual tracking. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE, 2014. 1090-1097
    [20] Wu Y, Lim J, Yang M H. Object tracking benchmark. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1834-1848 doi: 10.1109/TPAMI.2014.2388226
    [21] Comaniciu D, Ramesh V, Meer P. Real-time tracking of non-rigid objects using mean shift. In: Proceedings of the 2000 IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head Island, USA: IEEE, 2000. 2142-2149
    [22] Mueller M, Smith N, Ghanem B. Context-aware correlation filter tracking. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017. 1387-1395
    [23] Choi J, Chang H J, Yun S, Fischet J, Demiris Y, Choi J Y. Attentional correlation filter network for adaptive visual tracking. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017. 4828-4837
    [24] Wang N, Zhou W G, Tian Q, Hong R C, Wang M, Li H Q. Multi-cue correlation filters for robust visual tracking. In: Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018. 4844-4853
    [25] 陈志敏, 吴盘龙, 薄煜明, 田梦楚, 岳聪, 顾福飞. 基于自控蝙蝠算法智能优化粒子滤波的机动目标跟踪方法, 电子学报, 2018, 46(4): 886-894 doi: 10.3969/j.issn.0372-2112.2018.04.017

    Chen Zhi-Min, Wu Pan-Long, Bo Yu-Ming, Tian Meng-Chu, Yue Cong, Gu Fu-Fei. Adaptive control bat algorithm intelligent optimization particle filter for maneuvering target tracking. Acta Electronica Sinica, 2018, 46(4): 886-894 doi: 10.3969/j.issn.0372-2112.2018.04.017
    [26] 刘畅, 赵巍, 刘鹏, 唐降龙, 目标跟踪中辅助目标的选择、跟踪与更新, 自动化学报, 2018, 44(7): 1195-1211 doi: 10.16383/j.aas.2017.c160532

    Liu Chang, Zhao Wei, Liu Peng, Tang Xiang-Long. Auxiliary objects selecting, tracking and updating in target tracking. Acta Automatic Sinica, 2018, 44(7): 1195-1211 doi: 10.16383/j.aas.2017.c160532
  • 加载中
图(12) / 表(3)
计量
  • 文章访问数:  744
  • HTML全文浏览量:  363
  • PDF下载量:  159
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-03-15
  • 录用日期:  2019-01-09
  • 刊出日期:  2021-04-02

目录

    /

    返回文章
    返回