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Video anomaly detection is one of the hot research topics in computer visionnowadays, as abnormal events contain a high amount of information. Anomaliesare one of the main detection targets in surveillance systems, usually needingreal-time actions. Regarding the availability of labeled data for training(i.e., there is not enough labeled data for abnormalities), semi-supervisedanomaly detection approaches have gained interest recently. This paperintroduces the researchers of the field to a new perspective and reviews therecent deep-learning based semi-supervised video anomaly detection approaches,based on a common strategy they use for anomaly detection. Our goal is to helpresearchers develop more effective video anomaly detection methods. As theselection of a right Deep Neural Network plays an important role for severalparts of this task, a quick comparative review on DNNs is prepared first.Unlike previous surveys, DNNs are reviewed from a spatiotemporal featureextraction viewpoint, customized for video anomaly detection. This part of thereview can help researchers in this field select suitable networks fordifferent parts of their methods. Moreover, some of the state-of-the-artanomaly detection methods, based on their detection strategy, are criticallysurveyed. The review provides a novel and deep look at existing methods andresults in stating the shortcomings of these approaches, which can be a hintfor future works.

A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods