An RGB-T Object Tracking Method for Solving Camera Motion Based on Correlation Filter

The RGB-T tracking based on correlation filter frame is widely studied because of its high efficiency in most complex scenes. However, the performance of these trackers is limited when facing some specific challenges, such as camera motion and background clutter. This paper focuses on how to solve the camera motion in the framework of correlation filter. First, given the input infrared and RGB images, we extract different features and use multi-expert systems to select the experts, and then conduct decision fusion tracking. Secondly, we first design a feature matching algorithm to locate the target that shows excellent performance. Comprehensive experimental results show that the proposed tracker has better performance in both accuracy and robustness. Our results on VOT-RGBT2019 dataset also demonstrate that it solves the common camera motion challenges in RGB-T tracking.