Video Anomaly Event Detection (VAED) is the core technology of intelligent surveillance systems aiming to temporally or spatially locate anomalous events in videos. With the penetration of deep learning, the recent advances in VAED have diverged various routes and achieved significant success. However, most existing reviews focus on traditional and unsupervised VAED methods, lacking attention to emerging weakly-supervised and fully-unsupervised routes. Therefore, this review extends the narrow VAED concept from unsupervised video anomaly detection to Generalized Video Anomaly Event Detection (GVAED), which provides a comprehensive survey that integrates recent works based on different assumptions and learning frameworks into an intuitive taxonomy and coordinates unsupervised, weakly-supervised, fully-unsupervised, and supervised VAED routes. To facilitate future researchers, this review collates and releases research resources such as datasets, available codes, programming tools, and literature. Moreover, this review quantitatively compares the model performance and analyzes the research challenges and possible trends for future work.

翻译：视频异常事件检测（VAED）是智能监控系统的核心技术，旨在从时间或空间上定位视频中的异常事件。随着深度学习的渗透，VAED近年来的进展分化出多种研究路线并取得了显著成功。然而，现有综述大多聚焦于传统的无监督VAED方法，缺乏对新兴弱监督和全无监督路线的关注。因此，本综述将VAED的狭义概念从无监督视频异常检测拓展至广义视频异常事件检测（GVAED），通过系统整合基于不同假设和学习框架的最新研究，构建了直观的分类体系，并协调了无监督、弱监督、全无监督和监督四种VAED路线。为便利后续研究人员，本文整理并发布了数据集、可用代码、编程工具及文献等研究资源。此外，本文定量比较了模型性能，并分析了当前研究挑战及未来可能的发展趋势。