Context-Dependent Anomaly Action Recognition

We explore the problem of unsupervised anomaly action recognition, focusing on identifying abnormal human behavior that takes into account the surrounding context. Unlike conventional approaches that rely solely on observing the human action, we recognize that anomalies can be context-dependent—texting while driving, for example, may be considered an anomaly, but not if the car is parked safely and the driver is waiting to pick up a passenger. To this end, we propose a simple framework that processes concurrent data streams of human action and contextual information. To learn the concept of normal behavior, we employ normalizing flows to model the joint probability distribution over pre-defined normal actioncontext sample pairs. Our method is flexible in admitting the use of different action and context streams, including the ability to leverage pretrained models for extracting features describing the action and context. We conduct a series of experiments on publicly available data, demonstrating the effectiveness of our approach in identifying context-dependent anomaly actions, including application to driving scenarios. Our code is available at https://github.com/knbandit/CDAAR.