  Re-identification (ReID) in multi-object tracking (MOT) for UAVs in maritime
computer vision has been challenging for several reasons. More specifically,
short-term re-identification (ReID) is difficult due to the nature of the
characteristics of small targets and the sudden movement of the drone's gimbal.
Long-term ReID suffers from the lack of useful appearance diversity. In
response to these challenges, we present an adaptable motion-based MOT
algorithm, called Metadata Guided MOT (MG-MOT). This algorithm effectively
merges short-term tracking data into coherent long-term tracks, harnessing
crucial metadata from UAVs, including GPS position, drone altitude, and camera
orientations. Extensive experiments are conducted to validate the efficacy of
our MOT algorithm. Utilizing the challenging SeaDroneSee tracking dataset,
which encompasses the aforementioned scenarios, we achieve a much-improved
performance in the latest edition of the UAV-based Maritime Object Tracking
Challenge with a state-of-the-art HOTA of 69.5% and an IDF1 of 85.9% on the
testing split.
