LAUSR

Multiple object tracking (MOT) in satellite videos requires to detect all objects belonging to specified categories and identify each object, which plays a basic and necessary role in automatic driving, traffic surveillance, and smart city. The traditional MOT methods in satellite videos mostly follow the detection–association framework. However, the detection–association framework works under a strict assumption that all objects are correctly localized by the detector. In practice, MOT in satellite videos faces challenges such as low resolution, tiny objects, and the wide field of view, which leads to the degradation of detector performance. In order to reduce the impact of detector degradation, we propose a bidirectional MOT framework based on trajectory criteria (BMTC) in satellite videos. In BMTC, the single object tracking (SOT) tracker carries out locating the objects between consecutive frames and the detector is just used for finding new objects. Therefore, it is less dependent on the detector performance. According to the characteristics of satellite videos, the trajectory criteria are designed to control the state of the tracker, which includes trajectory density, the limit of consecutive virtual motion predictions, and trajectory similarity measurement. Invalid fragment trajectory backtracking is implemented to alleviate the misalignment caused by the above subsection trajectory criteria. The method is validated on the VISO benchmark and SkySat-1 dataset. The experimental results show the improvement of completeness and accuracy, and the proposed tracker achieves the state-of-the-art performance.