Mean Shift tracking algorithm with adaptive tracking window

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Mean Shift tracking algorithm with adaptive tracking window

Article | 更新时间：2020-08-12

- Mean Shift tracking algorithm with adaptive tracking window
- Optics and Precision Engineering Vol. 17, Issue 10, Pages: 2606-2611(2009)
- 作者机构：
  
  武汉大学电子信息学院
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391
- Received：12 November 2008，
  
  Revised：29 December 2008，
  
  Published Online：20 October 2009，
  
  Published：20 October 2009
- 稿件说明：
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颜佳,吴敏渊,陈淑珍,张青林. 跟踪窗口自适应的Mean Shift跟踪算法[J]. 光学精密工程, 2009,17(10):2606-2611

Mean Shift tracking algorithm with adaptive tracking window[J]. Optics and precision engineering, 2009, 17(10): 2606-2611.
颜佳,吴敏渊,陈淑珍,张青林. 跟踪窗口自适应的Mean Shift跟踪算法[J]. 光学精密工程, 2009,17(10):2606-2611 DOI：

Mean Shift tracking algorithm with adaptive tracking window[J]. Optics and precision engineering, 2009, 17(10): 2606-2611. DOI：

摘要

目的：针对传统Mean Shift跟踪算法中当目标发生形变时因跟踪窗不能动态改变导致跟偏甚至跟丢的缺陷，提出了一种新的跟踪窗口大小和方向自适应的改进算法。方法：首先采用跟踪窗口内协方差矩阵的主分量分析方法来计算跟踪目标的方向和尺寸大小；然后联合相似性度量和卡尔曼滤波器来更新跟踪窗口。结果：实验证明，本算法针对不断旋转和缩放的运动目标仍能对其进行准确实时的跟踪。结论：改进算法能够满足非刚体目标跟踪系统的要求。

Abstract

Objective: An new algorithm to estimation the scale and orientation of the tracking window is presented in order to solve the problem that traditional Mean Shift based tracking algorithm often fail when tracking a deformable target because of the unchanged tracking window. Method: Firstly

the principal components of the variance matrix are adopted to compute the scale and orientation of tracking target. Further

the similarity measure and Kalman filter are used to update the tracking window. Result: The experimental results show that the algorithm can be implemented in real-time and can adapt to the changes of scale and orientation of the moving object. Conclusion: It can satisfy the requirements of tracking non-rigid objects.

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