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Applied Optics

Applied Optics


  • Editor: Joseph N. Mait
  • Vol. 51, Iss. 21 — Jul. 20, 2012
  • pp: 5051–5059

Improved infrared target-tracking algorithm based on mean shift

Zhile Wang, Qingyu Hou, and Ling Hao  »View Author Affiliations

Applied Optics, Vol. 51, Issue 21, pp. 5051-5059 (2012)

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An improved IR target-tracking algorithm based on mean shift is proposed herein, which combines the mean-shift-based gradient-matched searching strategy with a feature-classification-based tracking algorithm. An improved target representation model is constructed by considering the likelihood ratio of the gray-level features of the target and local background as a weighted value of the original kernel histogram of the target region. An expression for the mean-shift vector in this model is derived, and a criterion for updating the model is presented. Experimental results show that the algorithm improves the shift weight of the target pixel gray level and suppresses background disturbance.

© 2012 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(100.4999) Image processing : Pattern recognition, target tracking

ToC Category:
Image Processing

Original Manuscript: December 8, 2011
Revised Manuscript: April 24, 2012
Manuscript Accepted: May 23, 2012
Published: July 11, 2012

Zhile Wang, Qingyu Hou, and Ling Hao, "Improved infrared target-tracking algorithm based on mean shift," Appl. Opt. 51, 5051-5059 (2012)

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