Kernel-based metric for performance evaluation of video infrared target tracking

Abstract

A kernel-based metric measuring tracking reliability that is based on discriminative components of a kernel target model and kernel mutual information is presented. The discriminative components of the kernel target model are selected by computing the log-likelihood ratios of classconditional sample densities of these components from a target region and background sampled region. The components selection process is embedded in a metric with kernel mutual information of the target regions of the initial frame and current frame in video infrared target tracking for online evaluation of the tracking reliability. Experimental results have shown that the metric can effectively characterize target tracking results as good or bad.

A novel method for quantifying target tracking difficulty of the infrared image sequence

“…We place a one-dimensional kernel centered on u and kernel values are used as p(v|u). Gaussian kernel is utilized here, and the conditional probability is given as follows [26]:…”