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文章基本信息

  • 标题:Real Time Object Tracking Using Different Mean Shift Techniques–a Review
  • 本地全文:下载
  • 作者:Snekha ; Chetna Sachdeva ; Rajesh Birok
  • 期刊名称:International Journal of Soft Computing & Engineering
  • 电子版ISSN:2231-2307
  • 出版年度:2013
  • 卷号:3
  • 期号:3
  • 页码:98-102
  • 出版社:International Journal of Soft Computing & Engineering
  • 摘要:The many different mean shift techniques for object tracking in real time are discussed in this paper. The mean shift is a non-parametric feature space analysis technique. It is a method for finding local maxima of a density function from given discrete data samples.There are several approaches that use the mean shift techniques for locating target objects. These techniques are taken from the literature dating back to the earliest methods. It is shown that at least 07 distinct methods have been introduced in the literature, with many variations on implementation. This paper should serve as a convenient reference for future work in real time object tracking.
  • 关键词:Mean shift; CAMshift; ABCshift; Path assigned;mean shift ; SOAMST and Fuzzy clustering mean shift
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