CrossTrack-UAV: A Real-World Dataset and Baseline for Cross-View Multi-Object Tracking Using Fixed-Wing UAVs

Abstract

Cross-view multi-object tracking (CVMOT) aims to simultaneously track a group of objects over time in each view and to associate the same objects across different views. This task is essential for swarm monitoring and has attracted considerable attention in the literature. However, most existing datasets focus on static cameras or cameras with stable motion in low-altitude environments, which hinders progress in developing methods for fixed-wing unmanned aerial vehicles (UAVs). To fill the gap, this work presents CrossTrack-UAV: the first real-world and large-scale CVMOT dataset using fixed-wing UAV swarms equipped with 2-axis gimbal cameras. CrossTrack-UAV was collected by four UAVs flying manually, covering five different cities in South Korea. The resulting dataset comprises 53 distinct scenarios and 1,272 cross-view tracks with 1,059,408 annotated 2D bounding boxes for 3 classes. It also contains synchronized metadata from UAVs, including GPS position, altitude, and gimbal orientations essential for extracting 3D geometric features. We introduce a CVMOT baseline built on the multi-sensor joint probabilistic data association filter (M-JPDAF). We extend M-JPDAF with spatial coherence processing to filter noisy and inconsistent data. Extensive experiments on CrossTrack-UAV demonstrate the effectiveness of the proposed baseline and the value of the benchmark.