Detecting abnormal events in video is commonly framed as a one-class classification task, where training videos contain only normal events, while test videos encompass both normal and abnormal events. In this scenario, anomaly detection is an open-set problem. However, some studies assimilate anomaly detection to action recognition. This is a closed-set scenario that fails to test the capability of systems at detecting new anomaly types. To this end, we propose UBnormal, a new supervised open-set benchmark composed of multiple virtual scenes for video anomaly detection. Unlike existing data sets, we introduce abnormal events annotated at the pixel level at training time, for the first time enabling the use of fully-supervised learning methods for abnormal event detection. To preserve the typical open-set formulation, we make sure to include disjoint sets of anomaly types in our training and test collections of videos. To our knowledge, UBnormal is the first video anomaly detection benchmark to allow a fair head-to-head comparison between one-class open-set models and supervised closed-set models, as shown in our experiments. Moreover, we provide empirical evidence showing that UBnormal can enhance the performance of a state-of-the-art anomaly detection framework on two prominent data sets, Avenue and ShanghaiTech. Our benchmark is freely available at https://github.com/lilygeorgescu/UBnormal.

翻译：视频中的异常事件检测通常被构建为一类分类任务，其中训练视频仅包含正常事件，而测试视频同时包含正常与异常事件。在此设定下，异常检测属于开放集问题。然而，部分研究将异常检测等同于动作识别，这是一种无法测试系统对新异常类型检测能力的封闭集场景。为此，我们提出UBnormal——一个由多个虚拟场景构成的新型监督式开放集基准，专用于视频异常检测。与现有数据集不同，我们在训练阶段引入了像素级标注的异常事件，首次实现了全监督学习方法在异常事件检测中的应用。为保留典型的开放集框架，我们确保训练集与测试集视频中包含互斥的异常事件类型集合。据我们所知，UBnormal是首个允许同类开放集模型与监督式封闭集模型进行公平直接比较的视频异常检测基准——实验已证实这一点。此外，我们通过实证表明，UBnormal能提升一个先进异常检测框架在两个主流数据集（Avenue和ShanghaiTech）上的性能。本基准于https://github.com/lilygeorgescu/UBnormal 免费提供。