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.

在这篇论文中，我们针对无人机移动场景中的多目标跟踪（MOT）的挑战，提出了一个新颖的通用视角匹配多目标跟踪（HomView-MOT）框架。我们引入了快速单应矩阵估计算法（FHE）来计算视频帧之间的单应矩阵，并利用多视角单应矩阵学习跨视角的标识特征，同时使用视角匹配滤波器（HMF）将不同时刻的物体边界框映射到一个共同的视角平面上进行真实的物体重叠度关联。经过广泛的实验证明，这些创新使HomView-MOT在著名的无人机MOT数据集VisDrone和UAVDT上实现了最先进的性能。

运动无人机中视角为中心的多目标跟踪与单应矩阵匹配