Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Driving

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.

Daniel Bogdoll and Jan Imhof contributed equally. Accepted for publication at BMVC 2024 RROW workshop. Won Best Paper Award