SiamMDM: An Adaptive Fusion Network With Dynamic Template for Real-Time Satellite Video Single Object Tracking

Tracking moving targets in satellite videos has attracted wide attention recently. However, the development of target tracking in satellite videos is much slower than that in general videos for the following key reasons. First, typical moving objects in satellite videos consist of few pixels and lose most of their appearance features, making it difficult for the tracker to distinguish the target from the background. Second, the appearance of satellite video objects often changes due to occlusion, illumination variation, or other factors. Classic Siamese tracking networks only use the first frame as the target template, leading to poor tracking results. Third, when the target is fully occluded, it is difficult for the tracker to recapture the target. To address the above problems, we propose a Siamese tracking network based on multiple(M) response map fusion and spatiotemporal constraints in this paper. By generating response maps at different layers of the tracking network and fusing them adaptively, small objects in the satellite videos can be tracked more accurately. Furthermore, a dynamic(D) template update strategy is proposed to cope with possible changes in the appearance of objects in satellite videos, preventing the high dependence on the initial frame. To recapture the target, a score-guided target motion(M) trajectory prediction model is proposed. We call the proposed Siamese tracking network SiamMDM for short. We conducted complete experiments on SatSOT and SV248S, two large satellite video target tracking datasets. The results show that our method achieves state-of-the-art tracking performance while running at over 110 FPS.