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Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Driving

2024-06-10 16:14:33
Daniel Bogdoll, Jan Imhof, Tim Joseph, J. Marius Z\"ollner

Abstract

In autonomous driving, the most challenging scenarios are the ones that 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. In this work, 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.

Abstract (translated)

在自动驾驶中,最具挑战性的场景是那些仅在它们的时间背景下才能被检测到的场景。大多数视频异常检测方法要么关注监视,要么关注交通事故,这两个子领域都属于自动驾驶。在这项工作中,我们提出了HF$^2$-VAD$_{AD",用于自动驾驶中的HF$^2$-VAD视频异常检测方法。我们从车辆的自我视角学习异常表示,并在罕见和关键场景下对像素级的异常进行评估。

URL

https://arxiv.org/abs/2406.06423

PDF

https://arxiv.org/pdf/2406.06423.pdf


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