View-Centric Multi-Object Tracking with Homographic Matching in Moving
  UAV
Deyi Ji Siqi Gao Lanyun Zhu Yiru Zhao Peng Xu Hongtao Lu Feng Zhao
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.