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一种基于加权时空上下文的鲁棒视觉跟踪算法

徐建强 陆耀

徐建强, 陆耀. 一种基于加权时空上下文的鲁棒视觉跟踪算法. 自动化学报, 2015, 41(11): 1901-1912. doi: 10.16383/j.aas.2015.c150073
引用本文: 徐建强, 陆耀. 一种基于加权时空上下文的鲁棒视觉跟踪算法. 自动化学报, 2015, 41(11): 1901-1912. doi: 10.16383/j.aas.2015.c150073
XU Jian-Qiang, LU Yao. Robust Visual Tracking via Weighted Spatio-temporal Context Learning. ACTA AUTOMATICA SINICA, 2015, 41(11): 1901-1912. doi: 10.16383/j.aas.2015.c150073
Citation: XU Jian-Qiang, LU Yao. Robust Visual Tracking via Weighted Spatio-temporal Context Learning. ACTA AUTOMATICA SINICA, 2015, 41(11): 1901-1912. doi: 10.16383/j.aas.2015.c150073

一种基于加权时空上下文的鲁棒视觉跟踪算法

doi: 10.16383/j.aas.2015.c150073
基金项目: 

国家自然科学基金(61273273,61271374),高等学校博士学科点专项科研基金(20121101110034)资助

详细信息
    作者简介:

    徐建强 北京理工大学计算机学院博士研究生.主要研究方向为目标跟踪,计算机视觉,模式识别.E-mail:xujq@bit.edu.cn

    通讯作者:

    陆耀 北京理工大学计算机学院教授.主要研究方向为神经网络,图像和信号处理,模式识别.本文通信作者.E-mail:vis_ly@bit.edu.cn

Robust Visual Tracking via Weighted Spatio-temporal Context Learning

Funds: 

Supported by National Natural Science Foundation of China (61273273, 61271374) and Research Fund for the Doctoral Program of Higher Education of China (20121101110034)

  • 摘要: 由于光照及外观变化、复杂背景、目标旋转与遮挡等因素的影响, 给实现鲁棒的视觉跟踪带来困难. 有效利用上下文(Context)中包含的有用信息有助于提升上述条件下视觉跟踪的鲁棒性. 时空上下文 (Spatio-temporal context, STC)算法是新近提出的一种基于时空上下文的目标跟踪算法, 它利用目标周围的稠密上下文信息, 取得了良好的跟踪效果. STC的不足是其同等对待整个上下文区域, 没有对上下文做进一步的区分, 减弱了上下文的作用. 本文采用动态分区处理思想, 根据上下文中不同区域与跟踪目标运动相似度大小, 赋予不同权值, 提出了基于加权时空上下文(Weighted spatio-temporal context, WSTC)的鲁棒视觉跟踪算法. 最后在公共数据集上进行的对比实验表明, 本文所提出的算法具有更好的跟踪效果和鲁棒性.
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出版历程
  • 收稿日期:  2015-02-04
  • 修回日期:  2015-07-11
  • 刊出日期:  2015-11-20

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