LAUSR

Using remote sensing video to monitor aircraft dynamics is significant for military applications, airport management, and aircraft rescue. The aircraft has a fixed size and obvious characteristics, so it is suitable for correlation filtering. Correlation filtering algorithms can extract features from input data to predict motion trajectories, and the calculation speed of correlation filterings is fast. Hence, such algorithms are advantageous for tracking targets in remote sensing images. In this article, an antidrift multifilter tracker based on a correlation filter and the Kalman filter is proposed for this purpose. This article proposes a temporal consistency-constrained background-aware correlation filter algorithm based on temporal regularization that resists the model drift caused by clouds by using motion information to correct it. Experimental results show that our proposed method shows improved antidrift performance compared with other advanced tracking methods in cases of cloud occlusion and stable performance in other complex conditions. We believe that our model will be helpful for researchers who are interested in object tracking in satellite video, especially for processing satellite video data with cloud occlusion.