This review extends the narrow VAED concept from unsupervised video anomaly detection to Generalized Video Anomaly Event Detection, which provides a comprehensive survey that integrates recent works based on different assumptions and learning frameworks into an intuitive taxonomy.

We design a causality-inspired representation consistency (CRC) framework to implicitly learn the unobservable causal variables of normality directly from available normal videos and detect abnormal events with the learned representation consistency.

We propose an appearance-motion prototype network (AMP-net) that uses external memories to record prototype features and augments the appearance-motion prototype with a spatial-temporal fusion.

We introduce a novel parallel model named Distributional and Spatial-Temporal Robust Representation (DSTRR), which combines automatic learning of statistical, spatial, and temporal features into a unified framework.