  Video anomaly detection is one of the hot research topics in computer vision
nowadays, as abnormal events contain a high amount of information. Anomalies
are one of the main detection targets in surveillance systems, usually needing
real-time actions. Regarding the availability of labeled data for training
(i.e., there is not enough labeled data for abnormalities), semi-supervised
anomaly detection approaches have gained interest recently. This paper
introduces the researchers of the field to a new perspective and reviews the
recent deep-learning based semi-supervised video anomaly detection approaches,
based on a common strategy they use for anomaly detection. Our goal is to help
researchers develop more effective video anomaly detection methods. As the
selection of a right Deep Neural Network plays an important role for several
parts of this task, a quick comparative review on DNNs is prepared first.
Unlike previous surveys, DNNs are reviewed from a spatiotemporal feature
extraction viewpoint, customized for video anomaly detection. This part of the
review can help researchers in this field select suitable networks for
different parts of their methods. Moreover, some of the state-of-the-art
anomaly detection methods, based on their detection strategy, are critically
surveyed. The review provides a novel and deep look at existing methods and
results in stating the shortcomings of these approaches, which can be a hint
for future works.
