Ling Jianguo, Liu Erqi, Liang Haiyan, Yang Jie
Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai, China.
Appl Opt. 2007 Jun 1;46(16):3239-52. doi: 10.1364/ao.46.003239.
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.
提出了一种红外目标跟踪框架，它由三个主要部分组成：均值漂移跟踪、其跟踪性能评估和位置校正。均值漂移跟踪算法是一种广泛使用的基于核的方法，因其效率和有效性而被开发用于初始跟踪。应用一个性能评估模块，通过基于核的度量对其跟踪性能进行在线评估，以便在基于核的跟踪框架内统一跟踪和性能度量。然后将跟踪性能评估结果输入到一个控制器中，在该控制器中做出是否触发位置校正过程的决策。位置校正模块采用一种匹配方法，该方法基于从局部复杂度加权协方差矩阵计算出的基于新特征值的相似性度量。给出了在实际红外图像序列上的实验结果，以证明所提方法的有效性。
