Abstract
The rapid movements and agile maneuvers of unmanned aerial vehicles (UAVs) induce significant observational challenges for multi-object tracking (MOT). However, existing UAV-perspective MOT benchmarks often lack these complexities, featuring predominantly predictable camera dynamics and linear motion patterns. To address this gap, we introduce DynUAV, a new benchmark for dynamic UAV-perspective MOT, characterized by intense ego-motion and the resulting complex apparent trajectories. The benchmark comprises 42 video sequences with over 1.7 million bounding box annotations, covering vehicles, pedestrians, and specialized industrial categories such as excavators, bulldozers and cranes. Compared to existing benchmarks, DynUAV introduces substantial challenges arising from ego-motion, including drastic scale changes and viewpoint changes, as well as motion blur. Comprehensive evaluations of state-of-the-art trackers on DynUAV reveal their limitations, particularly in managing the intertwined challenges of detection and association under such dynamic conditions, thereby establishing DynUAV as a rigorous benchmark. We anticipate that DynUAV will serve as a demanding testbed to spur progress in real-world UAV-perspective MOT, and we will make all resources available at link.
Abstract (translated)
无人驾驶飞行器（无人机）的快速移动和敏捷操作给多目标跟踪（MOT）带来了显著的观测挑战。然而，现有的以无人机视角为主的MOT基准测试往往缺乏这些复杂性，主要特征是可预测的摄像机动态和线性运动模式。为了填补这一空白，我们引入了DynUAV，这是一个新的动态无人机视角下的多目标跟踪（MOT）基准测试，它具有强烈的自我移动特性，并因此产生了复杂的视感轨迹。该基准测试包括42个视频序列，包含超过170万条边界框标注，涵盖了车辆、行人以及挖掘机、推土机和起重机等专门的工业类别。 与现有的基准测试相比，DynUAV引入了由自我的运动引起的显著挑战，包括剧烈的尺度变化和视角变化，以及运动模糊。对最先进的跟踪器在DynUAV上的全面评估揭示了它们在处理这种动态条件下的检测和关联交织挑战时的局限性，从而确立了DynUAV作为严格的基准测试的地位。 我们预计DynUAV将成为促进现实世界无人机视角MOT进步的严格测试平台，并将提供所有资源链接。
URL
https://arxiv.org/abs/2603.05970
