  In this paper, we address the challenge of multi-object tracking (MOT) in
moving Unmanned Aerial Vehicle (UAV) scenarios, where irregular flight
trajectories, such as hovering, turning left/right, and moving up/down, lead to
significantly greater complexity compared to fixed-camera MOT. Specifically,
changes in the scene background not only render traditional frame-to-frame
object IOU association methods ineffective but also introduce significant view
shifts in the objects, which complicates tracking. To overcome these issues, we
propose a novel universal HomView-MOT framework, which for the first time,
harnesses the view Homography inherent in changing scenes to solve MOT
challenges in moving environments, incorporating Homographic Matching and
View-Centric concepts. We introduce a Fast Homography Estimation (FHE)
algorithm for rapid computation of Homography matrices between video frames,
enabling object View-Centric ID Learning (VCIL) and leveraging multi-view
Homography to learn cross-view ID features. Concurrently, our Homographic
Matching Filter (HMF) maps object bounding boxes from different frames onto a
common view plane for a more realistic physical IOU association. Extensive
experiments have proven that these innovations allow HomView-MOT to achieve
state-of-the-art performance on prominent UAV MOT datasets VisDrone and UAVDT.
