X-MAN: Explaining multiple sources of anomalies in video
This addresses the need for explainable anomaly detection in video, which is crucial for practical applications where response depends on anomaly nature and severity, though it is incremental in improving interpretability.
The paper tackles the problem of detecting anomalies in video by proposing an interpretable method that explains the reasons behind detections, achieving competitive state-of-the-art performance on public datasets.
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.