Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Driving
<|reference_start|>Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Driving: 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.<|reference_end|>
@article{bogdoll2024hybrid,
    title={Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous
  Driving},
    author={Daniel Bogdoll, Jan Imhof, Tim Joseph, Svetlana Pavlitska, J. Marius
  Z"ollner},
    journal={arXiv preprint arXiv:2406.06423},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06423},
    primaryClass={cs.CV cs.RO}
}