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In recent years, algorithms for multiple object tracking tasks have benefitedfrom great progresses in deep models and video quality. However, in challengingscenarios like drone videos, they still suffer from problems, such as smallobjects, camera movements and view changes. In this paper, we propose a newmultiple object tracker, which employs Global Information And some Optimizingstrategies, named GIAOTracker. It consists of three stages, i.e., onlinetracking, global link and post-processing. Given detections in every frame, thefirst stage generates reliable tracklets using information of camera motion,object motion and object appearance. Then they are associated into trajectoriesby exploiting global clues and refined through four post-processing methods.With the effectiveness of the three stages, GIAOTracker achievesstate-of-the-art performance on the VisDrone MOT dataset and wins the 3rd placein the VisDrone2021 MOT Challenge.

GIAOTracker: A comprehensive framework for MCMOT with global information and optimizing strategies in VisDrone 2021