Video Anomaly Detection (VAD) serves as a pivotal technology in the intelligent surveillance systems, enabling the temporal or spatial identification of anomalous events within videos. While existing reviews predominantly concentrate on conventional unsupervised methods, they often overlook the emergence of weakly-supervised and fully-unsupervised approaches. To address this gap, this survey extends the conventional scope of VAD beyond unsupervised methods, encompassing a broader spectrum termed Generalized Video Anomaly Event Detection (GVAED). By skillfully incorporating recent advancements rooted in diverse assumptions and learning frameworks, this survey introduces an intuitive taxonomy that seamlessly navigates through unsupervised, weakly-supervised, supervised and fully-unsupervised VAD methodologies, elucidating the distinctions and interconnections within these research trajectories. In addition, this survey facilitates prospective researchers by assembling a compilation of research resources, including public datasets, available codebases, programming tools, and pertinent literature. Furthermore, this survey quantitatively assesses model performance, delves into research challenges and directions, and outlines potential avenues for future exploration.

翻译：视频异常检测（Video Anomaly Detection, VAD）作为智能监控系统中的关键技术，能够实现对视频中异常事件的时间或空间识别。现有综述文献主要关注传统无监督方法，却往往忽视了弱监督和全无监督方法的兴起。为弥补这一空白，本综述将VAD的传统范畴从无监督方法拓展至更广泛的广义视频异常事件检测（Generalized Video Anomaly Event Detection, GVAED）领域。通过巧妙整合基于不同假设和学习框架的最新成果，本文提出了一种直观的分类体系，系统梳理了无监督、弱监督、监督和全无监督VAD方法，阐明了这些研究路径之间的区别与内在联系。此外，本综述为未来研究者提供了丰富的科研资源汇编，包括公开数据集、可用代码库、编程工具及相关文献。同时，本文还对模型性能进行了量化评估，深入探讨了研究挑战与方向，并展望了未来可能的研究路径。