Object Tracking in Satellite Videos by Fusing the Kernel Correlation Filter and the Three-Frame-Difference Algorithm

Object tracking is a popular topic in the field of computer vision. The detailed spatial information provided by a very high resolution remote sensing sensor makes it possible to track targets of interest in satellite videos. In recent years, correlation filters have yielded promising results. However, in terms of dealing with object tracking in satellite videos, the kernel correlation filter (KCF) tracker achieves poor results due to the fact that the size of each target is too small compared with the entire image, and the target and the background are very similar. Therefore, in this letter, we propose a new object tracking method for satellite videos by fusing the KCF tracker and a three-frame-difference algorithm. A specific strategy is proposed herein for taking advantage of the KCF tracker and the three-frame-difference algorithm to build a strong tracker. We evaluate the proposed method in three satellite videos and show its superiority to other state-of-the-art tracking methods.