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

Abstract

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.

Unsupervised Conditional Diffusion Models in Video Anomaly Detection for Monitoring Dust Pollution

“…Generally, monitoring abnormal dust pollution is equivalent to video anomaly detection (VAD), which is popular but complex. Two kinds of approaches are prominent in the literature in this field: weakly supervised and unsupervised methods [1]. Weakly supervised methods model the abnormal probability of instances relying on labels [2][3][4].…”