The increasing popularity of compact and inexpensive cameras, e.g.~dash cameras, body cameras, and cameras equipped on robots, has sparked a growing interest in detecting anomalies within dynamic scenes recorded by moving cameras. However, existing reviews primarily concentrate on Video Anomaly Detection (VAD) methods assuming static cameras. The VAD literature with moving cameras remains fragmented, lacking comprehensive reviews to date. To address this gap, we endeavor to present the first comprehensive survey on Moving Camera Video Anomaly Detection (MC-VAD). We delve into the research papers related to MC-VAD, critically assessing their limitations and highlighting associated challenges. Our exploration encompasses three application domains: security, urban transportation, and marine environments, which in turn cover six specific tasks. We compile an extensive list of 25 publicly-available datasets spanning four distinct environments: underwater, water surface, ground, and aerial. We summarize the types of anomalies these datasets correspond to or contain, and present five main categories of approaches for detecting such anomalies. Lastly, we identify future research directions and discuss novel contributions that could advance the field of MC-VAD. With this survey, we aim to offer a valuable reference for researchers and practitioners striving to develop and advance state-of-the-art MC-VAD methods.

翻译：随着紧凑型低成本摄像机（如行车记录仪、随身摄像机和机器人搭载摄像机）的日益普及，对移动摄像机拍摄的动态场景中异常检测的研究兴趣显著增长。然而，现有综述主要聚焦于假设静态摄像机的视频异常检测（Video Anomaly Detection, VAD）方法。移动摄像机场景下的VAD研究仍较为分散，迄今缺乏系统性综述。为填补这一空白，本文首次提出移动摄像机视频异常检测（Moving Camera Video Anomaly Detection, MC-VAD）的全面综述。我们系统梳理了MC-VAD相关研究论文，批判性评估其局限性并强调相关挑战。研究涵盖三大应用领域：安防、城市交通和海洋环境，具体涉及六类任务。我们整理了25个公开可用的数据集，覆盖水下、水面、地面和空中四类环境，归纳了这些数据集对应或包含的异常类型，并总结了五类主要异常检测方法。最后，我们指明了未来研究方向，并探讨了可能推动MC-VAD领域发展的创新贡献。本综述旨在为致力于开发及推进MC-VAD前沿方法的研究者和实践者提供有价值的参考。