  Multiple object tracking (MOT) from unmanned aerial vehicle (UAV) platforms
requires efficient motion modeling. This is because UAV-MOT faces tracking
difficulties caused by large and irregular motion, and insufficient training
due to the motion long-tailed distribution of current UAV-MOT datasets.
Previous UAV-MOT methods either extract motion and detection features
redundantly or supervise motion model in a sparse scheme, which limited their
tracking performance and speed. To this end, we propose a flowing-by-detection
module to realize accurate motion modeling with a minimum cost. Focusing on the
motion long-tailed problem that were ignored by previous works, the flow-guided
margin loss is designed to enable more complete training of large moving
objects. Experiments on two widely open-source datasets show that our proposed
model can successfully track objects with large and irregular motion and
outperform existing state-of-the-art methods in UAV-MOT tasks.
