  In recent years, algorithms for multiple object tracking tasks have benefited
from great progresses in deep models and video quality. However, in challenging
scenarios like drone videos, they still suffer from problems, such as small
objects, camera movements and view changes. In this paper, we propose a new
multiple object tracker, which employs Global Information And some Optimizing
strategies, named GIAOTracker. It consists of three stages, i.e., online
tracking, global link and post-processing. Given detections in every frame, the
first stage generates reliable tracklets using information of camera motion,
object motion and object appearance. Then they are associated into trajectories
by exploiting global clues and refined through four post-processing methods.
With the effectiveness of the three stages, GIAOTracker achieves
state-of-the-art performance on the VisDrone MOT dataset and wins the 3rd place
in the VisDrone2021 MOT Challenge.
