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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: James C. Wyant
  • Vol. 46, Iss. 16 — Jun. 1, 2007
  • pp: 3239–3252

Infrared target tracking with kernel-based performance metric and eigenvalue-based similarity measure

Jianguo Ling, Erqi Liu, Haiyan Liang, and Jie Yang  »View Author Affiliations


Applied Optics, Vol. 46, Issue 16, pp. 3239-3252 (2007)
http://dx.doi.org/10.1364/AO.46.003239


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Abstract

An infrared target tracking framework is presented that consists of three main parts: mean shift tracking, its tracking performance evaluation, and position correction. The mean shift tracking algorithm, which is a widely used kernel-based method, has been developed for the initial tracking for its efficiency and effectiveness. A performance evaluation module is applied for the online evaluation of its tracking performance with a kernel- based metric to unify the tracking and performance metric within a kernel-based tracking framework. Then the tracking performance evaluation result is input into a controller in which a decision is made whether to trigger a position correction process. The position correction module employs a matching method with a new eigenvalue-based similarity measure computed from a local complexity degree weighted covariance matrix. Experimental results on real-life infrared image sequences are presented to demonstrate the efficacy of the proposed method.

© 2007 Optical Society of America

OCIS Codes
(100.0100) Image processing : Image processing
(100.5010) Image processing : Pattern recognition

ToC Category:
Image Processing

History
Original Manuscript: July 17, 2006
Revised Manuscript: October 30, 2006
Manuscript Accepted: January 8, 2007
Published: May 15, 2007

Citation
Jianguo Ling, Erqi Liu, Haiyan Liang, and Jie Yang, "Infrared target tracking with kernel-based performance metric and eigenvalue-based similarity measure," Appl. Opt. 46, 3239-3252 (2007)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-46-16-3239


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