Infrared target tracking with kernel-based performance metric and eigenvalue-based similarity measure |
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Authors: | Ling Jianguo Liu Erqi Liang Haiyan Yang Jie |
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Affiliation: | Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai, China. lingjianguo76@163.com |
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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. |
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