تقرير
Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Driving
| العنوان: | Hybrid Video Anomaly Detection for Anomalous Scenarios in Autonomous Driving |
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| المؤلفون: | Bogdoll, Daniel, Imhof, Jan, Joseph, Tim, Zöllner, J. Marius |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics |
| الوصف: | 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. Comment: Daniel Bogdoll and Jan Imhof contributed equally |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.06423 |
| رقم الأكسشن: | edsarx.2406.06423 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |