View-Centric Multi-Object Tracking with Homographic Matching in Moving UAV
<|reference_start|>View-Centric Multi-Object Tracking with Homographic Matching in Moving UAV: 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.<|reference_end|>
@article{ji2024view-centric,
    title={View-Centric Multi-Object Tracking with Homographic Matching in Moving
  UAV},
    author={Deyi Ji, Siqi Gao, Lanyun Zhu, Qi Zhu, Yiru Zhao, Peng Xu, Hongtao Lu,
  Feng Zhao, Jieping Ye},
    journal={arXiv preprint arXiv:2403.10830},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10830},
    primaryClass={cs.CV}
}