## 5 Conclusions  
We design a novel tracking algorithm that improves the robustness and tracking accuracy of object tracking in satellite video under complex background and object occlusion. Our method relies on a correlation filter embedding a fusion strategy and a motion trajectory correction scheme. The developed technique enhances the features' effective representation by extracting depth features and utilizes an improved Kalman filter to mitigate the tracking drifts in satellite videos through trajectory compensation and correction.  
For the experiments, we choose eight satellite videos. Considering tracking moving vehicles and planes, our proposed algorithm affords a better tracking performance against current tracking algorithms, solving the problem of object occlusion. Compared with the SiamRPN tracking algorithm, the tracking accuracy of our method is slightly lower in some cases but still affords a good tracking performance.  
The applications related to video satellites will become more and more widespread, and object tracking in satellite videos will gradually develop depending on the various needs. However, despite current methods affecting object tracking, additional work and innovation are acquired to improve accuracy, robustness, and object tracking performance. Although our method can solve the problem of low object tracking accuracy or tracking failure in satellite videos with complex backgrounds and object occlusion, there are some limitations. Firstly, suppose the object has been occluded before the adaptive Kalman filter converges. In that case, the prediction of the KCF tracker is not accurate when the object is occluded because the Kalman filter has not yet converged, which will result in low accuracy of the object tracking results or even tracking failure. Secondly, using hand-crafted features (HOG features) and depth features for object tracking is more computationally intensive and difficult to achieve real-time tracking on satellites. It would be very meaningful to design a robust feature with less computational complexity so that it is possible to track objects in real-time on satellites. Future work shall focus on object rotation and scale variation problems to improve object tracking accuracy further.  
Conceptualization, [PERSON] ([PERSON]) and Y.L. ([PERSON]); methodology, Y.J. and C.L.; writing--original draft, [PERSON] ([PERSON]) and C.L.; writing--review and editing, [PERSON] ([PERSON]), Z.L. and X.Y. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (NSFC) (No. 61805283). The authors are grateful to Chang Guang Satellite Technology Co., Ltd. for providing the satellite videos used in our paper, without which our research would not have been possible. The authors also thank the anonymous reviewers and Academic Editors for providing valuable comments, which have been very beneficial for improving the manuscript. The authors would like to express their gratitude to EditSprings ([[https://www.editsprings.com/](https://www.editsprings.com/)]([https://www.editsprings.com/](https://www.editsprings.com/)) (accessed on 20 November 2021)) for the expert linguistic services provided. The authors declare no conflict of interest.