A critical study on the recent deep learning based semi-supervised video anomaly detection methods

Video anomaly detection (VAD) is currently a trending research area within computer vision, given that anomalies form a key detection objective in surveillance systems, often requiring immediate responses. The primary challenges associated with video anomaly detection tasks stem from the scarcity of anomaly samples and the context-dependent nature of anomaly definitions. In light of the limited availability of labeled data for training (specifically, a shortage of labeled data for abnormalities), there has been a growing interest in semi-supervised anomaly detection methods. These techniques work by identifying anomalies through the detection of deviations from normal patterns. This paper provides a new perspective to researchers in the field, by categorizing semi-supervised VAD methods according to the proxy task type they employ to model normal data and consequently to detect anomalies. It also reviews recent deep learning based semi-supervised VAD methods, emphasizing their common tactic of slightly overfitting their models on normal data using a proxy task to detect anomalies. Our goal is to help researchers develop more effective video anomaly detection methods. As the selection of a right Deep Neural Network (DNN) plays an important role in several parts of this task, a quick comparative review on DNNs is also included. 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 select suitable networks for different parts of their methods. 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.