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Anomaly detection in videos has been attracting an increasing amount ofattention. Despite the competitive performance of recent methods on benchmarkdatasets, they typically lack desirable features such as modularity,cross-domain adaptivity, interpretability, and real-time anomalous eventdetection. Furthermore, current state-of-the-art approaches are evaluated usingthe standard instance-based detection metric by considering video frames asindependent instances, which is not ideal for video anomaly detection.Motivated by these research gaps, we propose a modular and unified approach tothe online video anomaly detection and localization problem, called MOVAD,which consists of a novel transfer learning based plug-and-play architecture, asequential anomaly detector, a mathematical framework for selecting thedetection threshold, and a suitable performance metric for real-time anomalousevent detection in videos. Extensive performance evaluations on benchmarkdatasets show that the proposed framework significantly outperforms the currentstate-of-the-art approaches.

A Modular and Unified Framework for Detecting and Localizing Video Anomalies