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基于尺度自适应和跟踪框自转的视频目标跟踪 被引量：8

Video target tracking based on scale adaptation and tracking box rotations
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摘要 在高空运动变焦摄像机视频监控目标的自动识别跟踪中,跟踪目标背景、跟踪目标尺寸和跟踪目标相对背景运动的方位角都在实时变化,为解决常规Mean Shift目标跟踪算法在面临上述快速变化时容易出现的目标跟踪丢失问题,在Mean Shift目标跟踪算法的基础上,考虑跟踪目标的变尺度、长宽比和方位角等因素,提出了改进的基于尺度自适应和自转跟踪框策略的视频目标跟踪算法,实际场景下的实验结果表明:该算法具有较好的准确性和实时性,满足视频目标实时跟踪的应用需求。 A video target tracking algorithm was developed for automatic high-altitude tracking using a camera with a zoom lense for tracking real-time changes in the azimuth angle of the target and motion relative to the background.The conventional Mean Shift target tracking algorithm to reduce target loss due to these rapid changes uses metrics such as the target length-width ratio and azimuth angle in the Mean Shift algorithm.The algorithm incorporates scale adaptation and rotation tracking.Tests with actual videos indicate that this algorithm is accurate and fast enough for real-time target tracking.
作者 马伟 陈建国 张毛磊
机构地区 清华大学工程物理系
出处 《清华大学学报（自然科学版）》 EI CAS CSCD 北大核心 2012年第1期92-95,共4页 Journal of Tsinghua University(Science and Technology)
基金 国家自然科学基金资助项目(70801039) 国家科技支撑计划项目(2008BAB29B07)
关键词 视频监控 目标跟踪 尺度自适应 跟踪框自转 video monitoring target tracking scale adaptation tracking box rotation
分类号 TP751.1 [自动化与计算机技术—检测技术与自动化装置]
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参考文献11

  • 1Comaniciu D, Ramesh V, Meer P. Real-time tracking of non-rigid objects using mean shift [C]// IEEE International Proceeding on Computer Vision and Pattern Recognition. Stoughton: Printing House, 2000, (2) :142- 149.
  • 2Wang J O, Yagi Y. Integrating color and shape texture features for adaptive real-time object tracking [J]. IEEE Transactions on Image Processing, 2008, 17(2): 235- 240.
  • 3Zhou H, Yuan Y, Shi C. Object tracking using shift features and mean shift [J]. Computer Vision and Image Understanding, 2009, 113(3) : 345-352.
  • 4Deguchi K, Kawanaka O, Okatani T. Object tracking by the Mean Shift of regional color distribution combined with the particle-filter algorithm [C]// International Conference on Pattern Recognition. Cambridge, UK: IEEE Press, 2004.- 506 - 509.
  • 5Yang C J, Duraiswami R, and Davis L. Efficient spatial feature tracking via a new similarity measure [C]// Proceeding of IEEE Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE Computer Society, 2005:176 - 183.
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  • 7彭宁嵩,杨杰,刘志,张风超.Mean-Shift跟踪算法中核函数窗宽的自动选取[J].软件学报,2005,16(9):1542-1550. 被引量：165
  • 8Comaniciu D, Ramesh V, Meer P. Kernel-based object tracking [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5) : 564 - 577.
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二级参考文献10

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共引文献164

同被引文献105

引证文献8

二级引证文献42

清华大学学报（自然科学版）
2012年 第1期

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