Deep Siamese Network With Motion Fitting for Object Tracking in Satellite Videos

Abstract

With the advancement in remote sensing satellite technology, object tracking in satellite videos has become an emerging research field. However, due to small object size, little appearance features, and poor distinguishability between targets and the background, traditional trackers with handcraft visual features achieve poor results in satellite videos. Deep neural networks have shown powerful potential for object tracking in ordinary videos but remain developing in satellite videos. In this letter, a Siamese network and a motion regression network are adopted to form a two-stream deep neural network (SRN) for satellite object tracking, which simultaneously utilizes appearance and motion features. Besides, a trajectory fitting motion (TFM) model based on history trajectories is also employed to further alleviate model drift. Comprehensive experiments demonstrate that the proposed method performs favorably compared with the state-of-the-art tracking methods.