打破平滑运动假设：面向复杂恶劣条件下多目标跟踪的无人机基准
Breaking Smooth-Motion Assumptions: A UAV Benchmark for Multi-Object Tracking in Complex and Adverse Conditions
为无人机视角多目标跟踪提供了包含剧烈自运动、尺度变化和运动模糊的严苛基准，可用于评估和提升跟踪器在动态复杂场景下的检测与关联鲁棒性。
原文依据 we introduce DynUAV, a new benchmark for dynamic UAV-perspective MOT, characterized by intense ego-motion and the resulting complex apparent trajectories.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论无人机的快速运动和敏捷机动给多目标跟踪带来了显著的观测挑战。然而，现有的无人机视角多目标跟踪基准往往缺乏这些复杂性，主要呈现可预测的相机动态和线性运动模式。为填补这一空白，我们提出了DynUAV，一个面向动态无人机视角多目标跟踪的新基准，其特点是强烈的自运动以及由此产生的复杂表观轨迹。该基准包含42个视频序列和超过170万个边界框标注，覆盖车辆、行人以及挖掘机、推土机和起重机等专业工业类别。与现有基准相比，DynUAV引入了由自运动带来的实质性挑战，包括剧烈的尺度变化和视角变化，以及运动模糊。在DynUAV上对最优跟踪器进行的综合评估揭示了它们的局限性，尤其是在这种动态条件下处理检测与关联相互交织的挑战方面，从而确立了DynUAV作为一个严谨基准的地位。我们预期DynUAV将作为一个严苛的测试平台，推动真实世界无人机视角多目标跟踪的进步，所有资源将在https://github.com/kxzhang-lab/DynUAV上公开。
英文原文摘要 1320 字符
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 https://github.com/kxzhang-lab/DynUAV.