计算机科学 ›› 2011, Vol. 38 ›› Issue (3): 271-274.

• 图形图像 • 上一篇    下一篇

基于模板更新的自适应Mean-shift跟踪算法

李勇勇,谭毅华,田金文   

  1. (华中科技大学图像识别与人工智能研究所 武汉430074);(华中科技大学多谱信息处理技术国家级重点实验室 武汉430074)
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受航空科学基金(20085179008)资助。

Template Updating Based Adaptive Tracking Algorithm Using Mean-shift

LI Yong-yong,TAN Yi-hua,TIAN Jin-wen   

  • Online:2018-11-16 Published:2018-11-16

摘要: 提出了一种利用Mcan-shift算法处理目标跟踪定位,并以SIFT特征点匹配结果的最小二乘模型来求解缩放系数和更新目标模型的自适应跟踪方法。该方法实现了目标的快速跟踪,解决了模板更新和目标的尺度缩放问题。实验结果表明,该算法在处理目标尺度变化较大的情况下具有很强的鲁棒性。

关键词: 目标跟踪,均值漂移,SIFT点,尺度变化,最小二乘模型

Abstract: This paper proposed an adaptive tracking algorithm in which the Mean-shift algorithm is taken to obtain target position. Among this framework, the target model is updated by analyzing the results of SIFT points matching using least sctuares model. This algorithm can fast track target with the advantages that the template updating and the finding of scale coefficients arc implemented at the same time. The experimental results show that the algorithm is very robust to the cases that the scales of target vary largely.

Key words: Target tracking, Mean-shift, SIFT point, Scales variant, Least squares mode

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