Recent advances in video anomaly detection (VAD) mainly focus on ground-based surveillance or unmanned aerial vehicle (UAV) videos with static backgrounds, whereas research on UAV videos with dynamic backgrounds remains limited. Unlike static scenarios, dynamically captured UAV videos exhibit multi-source motion coupling, where the motion of objects and UAV-induced global motion are intricately intertwined. Consequently, existing methods may misclassify normal UAV movements as anomalies or fail to capture true anomalies concealed within dynamic backgrounds. Moreover, many approaches do not adequately address the joint modeling of inter-frame continuity and local spatial correlations across diverse temporal scales. To overcome these limitations, we propose the Frequency-Assisted Temporal Dilation Mamba (FTDMamba) network for UAV VAD, including two core components: (1) a Frequency Decoupled Spatiotemporal Correlation Module, which disentangles coupled motion patterns and models global spatiotemporal dependencies through frequency analysis; and (2) a Temporal Dilation Mamba Module, which leverages Mamba's sequence modeling capability to jointly learn fine-grained temporal dynamics and local spatial structures across multiple temporal receptive fields. Additionally, unlike existing UAV VAD datasets which focus on static backgrounds, we construct a large-scale Moving UAV VAD dataset (MUVAD), comprising 222,736 frames with 240 anomaly events across 12 anomaly types. Extensive experiments demonstrate that FTDMamba achieves state-of-the-art (SOTA) performance on two public static benchmarks and the new MUVAD dataset. The code and MUVAD dataset will be available at: https://github.com/uavano/FTDMamba.

翻译：近年来视频异常检测（VAD）的研究主要集中于地面监控或背景静态的无人机视频，而针对动态背景无人机视频的研究仍较为有限。与静态场景不同，动态拍摄的无人机视频呈现多源运动耦合特性，其中目标运动与无人机自身引发的全局运动错综交织。因此，现有方法可能将正常的无人机运动误判为异常，或难以捕捉隐藏在动态背景中的真实异常。此外，许多方法未能充分解决跨多时间尺度的帧间连续性与局部空间相关性的联合建模问题。为克服这些局限，我们提出面向无人机VAD的频率辅助时序膨胀Mamba网络（FTDMamba），其包含两个核心组件：（1）频率解耦时空关联模块，通过频域分析解耦混合运动模式并建模全局时空依赖；（2）时序膨胀Mamba模块，利用Mamba的序列建模能力，在多重时序感受野中联合学习细粒度时间动态与局部空间结构。此外，区别于现有聚焦静态背景的无人机VAD数据集，我们构建了大规模动态无人机VAD数据集（MUVAD），包含222,736帧图像、涵盖12类异常类型的240个异常事件。大量实验表明，FTDMamba在两个公开静态基准数据集及新构建的MUVAD数据集上均取得了最先进的性能。代码与MUVAD数据集将发布于：https://github.com/uavano/FTDMamba。