  Our objective is to detect anomalies in video while also automatically
explaining the reason behind the detector's response. In a practical sense,
explainability is crucial for this task as the required response to an anomaly
depends on its nature and severity. However, most leading methods (based on
deep neural networks) are not interpretable and hide the decision making
process in uninterpretable feature representations. In an effort to tackle this
problem we make the following contributions: (1) we show how to build
interpretable feature representations suitable for detecting anomalies with
state of the art performance, (2) we propose an interpretable probabilistic
anomaly detector which can describe the reason behind it's response using high
level concepts, (3) we are the first to directly consider object interactions
for anomaly detection and (4) we propose a new task of explaining anomalies and
release a large dataset for evaluating methods on this task. Our method
competes well with the state of the art on public datasets while also providing
anomaly explanation based on objects and their interactions.
