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