Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Driving

Daniel Bogdoll, Jan Imhof, Tim Joseph, J. Marius Zöllner

Abstract: In autonomous driving, the most challenging scenarios can only be detected within their temporal context. Most video anomaly detection approaches focus either on surveillance or traffic accidents, which are only a subfield of autonomous driving. We present HF$^2$-VAD$_{AD}$, a variation of the HF$^2$-VAD surveillance video anomaly detection method for autonomous driving. We learn a representation of normality from a vehicle's ego perspective and evaluate pixel-wise anomaly detections in rare and critical scenarios.