title: Infrared target tracking with kernel-based performance metric and eigenvalue-based similarity measure.
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.
authors.affiliation: Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai, China. lingjianguo76@163.com
authors.forename: Jianguo
authors.initials: J
authors.lastname: Ling
authors.forename: Erqi
authors.initials: E
authors.lastname: Liu
authors.forename: Haiyan
authors.initials: H
authors.lastname: Liang
authors.forename: Jie
authors.initials: J
authors.lastname: Yang