Adaptive mean shift for target- tracking in FLIR imagery

Abstract

In this paper, we present a novel adaptive mean-shift tracker for tracking moving targets in the FLIR imagery, captured from an airborne moving platform. First, each target's position is manually marked at the first frame to initialize the adaptive mean-shift based tracker. For each target, multiple different features are extracted from both the targets and background during tracking, and an on-line feature ranking method is deployed to adaptively select the most discriminative feature for the mean-shift iteration. In addition, to compensate the motion of the moving platform, a block matching method is applied to compute the motion vector, which will be used in the RANSAC algorithm to estimate the affine model for global motion. We test our method on the AMCOM FLIR data set, the results indicate that our Adaptive mean-shift tracker can track each target accurately and robustly.