Video Anomaly Detection (VAD) finds widespread applications in security surveillance, traffic monitoring, industrial monitoring, and healthcare. Despite extensive research efforts, there remains a lack of concise reviews that provide insightful guidance for researchers. Such reviews would serve as quick references to grasp current challenges, research trends, and future directions. In this paper, we present such a review, examining models and datasets from various perspectives. We emphasize the critical relationship between model and dataset, where the quality and diversity of datasets profoundly influence model performance, and dataset development adapts to the evolving needs of emerging approaches. Our review identifies practical issues, including the absence of comprehensive datasets with diverse scenarios. To address this, we introduce a new dataset, Multi-Scenario Anomaly Detection (MSAD), comprising 14 distinct scenarios captured from various camera views. Our dataset has diverse motion patterns and challenging variations, such as different lighting and weather conditions, providing a robust foundation for training superior models. We conduct an in-depth analysis of recent representative models using MSAD and highlight its potential in addressing the challenges of detecting anomalies across diverse and evolving surveillance scenarios. [Project website: https://msad-dataset.github.io/]

翻译：视频异常检测（VAD）在安防监控、交通监测、工业监控和医疗健康等领域具有广泛应用。尽管已有大量研究，但仍缺乏能为研究者提供深刻见解的简明综述。此类综述可作为快速参考，帮助把握当前挑战、研究趋势与未来方向。本文提出这样一篇综述，从多角度审视模型与数据集。我们强调模型与数据集之间的关键关联：数据集的质量与多样性深刻影响模型性能，而数据集的发展也需适应新兴方法不断变化的需求。本综述指出了若干实际问题，包括缺乏涵盖多样化场景的综合性数据集。为此，我们引入了一个新数据集——多场景异常检测数据集（MSAD），该数据集包含从不同摄像机视角采集的14个独立场景。我们的数据集具有丰富的运动模式与具有挑战性的变化因素（如不同光照与天气条件），为训练更优模型提供了坚实基础。我们利用MSAD对近期代表性模型进行了深入分析，并凸显了其在应对多样化且动态演进的监控场景中异常检测挑战方面的潜力。[项目网站：https://msad-dataset.github.io/]