  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.
