随着监控技术的不断发展，监控摄像头已经被广泛部署到各种场景中。手动检测视频异常情况已经变得不可能。因此，作为智能监控系统核心的视频异常检测技术正在受到广泛关注和研究。随着深度学习的发展，视频异常检测领域取得了显著的成就，并涌现出许多新的异常检测方法。梳理了应用在不同数据类型上的无监督和弱监督视频异常检测学习方法，分析现有方法的贡献，并比较不同模型的性能。此外，还整理了一些常用的和新发布的数据集，并总结了未来工作要面临的挑战和发展趋势。

With the continuous development of monitoring technology, surveillance cameras have been widely deployed in various scenarios. Manual detection of video abnormality has become impossible. Therefore, video anomaly detection technology, as the core of intelligent surveillance systems, is receiving extensive attention and research. With the development of deep learning, the field of video anomaly detection has made significant achievements and has emerged many new anomaly detection methods. Unsupervised and weakly supervised video anomaly detection learning methods applied to various data types were sorted out, the contributions of existing methods were analyzed, and the performance of different models was compared. In addition, some commonly used and newly released datasets have also been compiled, and the challenges and development trends that future work will face have been summarized.