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