SENSE-VAD：面向自动驾驶的情感和语义视频异常检测
SENSE-VAD: Sentient and Semantic Video Anomaly Detection for Autonomous Driving
AI总结：
针对自动驾驶中社会关系导致的异常（如追逐、被抱等），提出首个合成视频异常检测基准SENSE-VAD，利用CARLA和UE生成多类别社交异常场景，并分离社交异常与运动异常，评估现有方法发现其难以解决该挑战。
摘要：
自动驾驶车辆（AV）不仅需要应对基于运动的危险，还必须处理社会复杂情境，其危险性由主体间关系而非单纯的移动统计数据构成。一个孩子逃离监护人、一个人被另一个人抱着、或者一个追逐者穿过人行道追赶行人，这些在社会情境中都是异常的，但当前异常检测器并未产生明显的运动信号来标记这些情况。我们引入了SENSE-VAD，这是第一个专门围绕社会复杂异常设计的自动驾驶合成视频异常检测基准。使用CARLA模拟器和Unreal Engine（UE），我们生成了跨多个类别的不同异常场景：个体行为、群体行为、人-物交互、骑行者交互、车辆与智能体，每个场景都带有每帧二元标签。一个关键设计原则是将社交异常与基于运动或基于外观的异常分离：许多场景涉及物体的运动，这些运动在孤立情况下看似正常，但在关系情境中却是异常的。我们还提供了真实世界的正常和异常视频作为模拟到真实的迁移探针。我们评估了最先进的视频异常检测基线，并证明了社会复杂异常构成了一个独特且尚未解决的挑战。我们的数据集、注释和生成代码已公开。
英文摘要：
Autonomous vehicles (AVs) must navigate not only motion-based hazards but also socially complex situations whose danger is constituted by inter-agent relationships rather than movement statistics alone. A child running away from a guardian, a person being carried by another, or a pursuer chasing a pedestrian across a sidewalk are all anomalous in social context, yet none produces an obvious motion signal that current anomaly detectors are equipped to flag. We introduce SENSE-VAD, the first synthetic video anomaly detection benchmark for autonomous driving explicitly designed around socially complex anomalies. Using the CARLA simulator and Unreal Engine (UE), we generate distinct anomaly scenarios across multiple categories: individual behaviors, group behaviors, person--object interactions, cyclist interactions, vehicle & agent, each annotated with per-frame binary labels. A key design principle is the separation of social anomaly from motion-based or appearance-based anomaly: many scenarios involve motion of objects that appears unremarkable in isolation but is anomalous in relational context. We additionally provide real-world normal and anomalous videos as a sim-to-real transfer probe. We evaluate state-of-the-art video anomaly detection baselines and demonstrate that socially complex anomalies constitute a distinct and currently unsolved challenge. Our dataset, annotations, and generation code are publicly available.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
