A Modular and Unified Framework for Detecting and Localizing Video Anomalies
This work addresses the need for more adaptable and interpretable anomaly detection in videos, though it appears incremental as it builds on existing methods to enhance features like cross-domain adaptivity.
The paper tackles the problem of online video anomaly detection and localization by proposing a modular framework (MOVAD) that addresses limitations like lack of modularity and real-time capabilities, achieving significant performance improvements over state-of-the-art methods on benchmark datasets.
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.