Motion-Guided Multiobject Tracking Model for High-Speed Aerial Objects in Satellite Videos

With the continuous improvement of remote sensing satellite technology for Earth observation, satellite video object tracking has become a new research field. Tracking aerial objects in satellite video facilitates highly accurate positioning over a wide coverage area which plays a crucial role in remote sensing scenes such as military analysis and disaster prevention. However, for such a data-driven task, most previous satellite video datasets focus on ground object tracking and lack attention to high-speed aerial objects. In this regard, a high-resolution satellite video dataset (AIR-HSAO) for detecting and tracking high-speed aerial objects is built including satellite videos from over 20 regions worldwide, with detailed annotations for approximately 1400 airplane trajectories. Based on the AIR-HSAO, an end-to-end online multiobject tracking method called MGTrack is proposed for high-speed aerial objects. Specifically, the motion prediction (MP) module is proposed to implicitly model object motion and associate objects. Guided by the predicted offsets, the feature warping and fusion (FWF) module aligns and fuses temporal features, fully exploiting the temporal correlations of the video sequence to obtain more accurate feature representations for high-speed objects. Additionally, a trajectory-aware size-enhanced loss is designed to handle the problem of discontinuous trajectories and small-scale objects. Experimental results on AIR-HSAO demonstrate that the proposed MGTrack improves the tracking accuracy by 2.3 MOTA and 1.6 IDF1 compared to the state-of-the-art tracking methods. The code is released at
https://github.com/RenLibo-aircas/AIR-HSAT-dataset
.