  Video anomaly detection is an essential yet challenging task in the
multimedia community, with promising applications in smart cities and secure
communities. Existing methods attempt to learn abstract representations of
regular events with statistical dependence to model the endogenous normality,
which discriminates anomalies by measuring the deviations to the learned
distribution. However, conventional representation learning is only a crude
description of video normality and lacks an exploration of its underlying
causality. The learned statistical dependence is unreliable for diverse regular
events in the real world and may cause high false alarms due to
overgeneralization. Inspired by causal representation learning, we think that
there exists a causal variable capable of adequately representing the general
patterns of regular events in which anomalies will present significant
variations. Therefore, 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. Extensive
experiments show that the causality-inspired normality is robust to regular
events with label-independent shifts, and the proposed CRC framework can
quickly and accurately detect various complicated anomalies from real-world
surveillance videos.
