摘要：视频异常检测（Video Anomaly Detection, VAD）是一项受到活跃研究的任务，在公共安防监控和道路交通安全等典型场景中拥有广泛的应用。该任务同样与机械臂交互操作密切相关，具备多种下游应用价值，包括学习更强的交互与操作能力、在异常发生时触发恢复流程等。尽管意义重大，但由于可用资源匮乏，机械臂操作视频中的异常检测研究一直受到很大限制。为此，我们推出了 RoboVAD——一个用于视频异常检测的大规模基准数据集，包含极具挑战性的跨领域评估场景，其中某些动作（机械臂执行的任务）和异常类型（执行特定任务时发生的错误）在训练期间完全保持未见状态。RoboVAD 旨在真实场景下评测 VAD 方法，在这些场景中，机械臂可能会执行预料之外的任务，从而遇到全新的异常类型。我们训练并评估了多种最先进的（SOTA）VAD 方法，其中包括一种专为机械臂操作适配的新方法。尽管该方法表现优于许多现有的前沿竞品，但在最具挑战性的评估设置下，所有方法的微平均帧级 AUC（micro-averaged frame-level AUC）阈值均未能达到 70%，充分证实了该基准的高难度。我们已在此链接公开发布了数据集和代码。

Abstract:Video anomaly detection (VAD) is an actively studied task, having wide applications in typical scenarios such as public surveillance and road traffic safety. The task is also relevant for robotic arm interactions, where it has several downstream applications, including learning better interaction and manipulation abilities, triggering recovery procedures when anomalies occur, etc. Despite its relevance, the exploration of anomaly detection in robotic arm manipulation videos is limited by the low number of available resources. To this end, we introduce RoboVAD, a large-scale benchmark for video anomaly detection that comprises challenging cross-domain evaluation scenarios, where certain actions (tasks executed by a robotic arm) and anomaly types (mistakes that occur while performing certain tasks) remain unseen during training. RoboVAD is designed to benchmark VAD methods in realistic scenarios, where robotic arms can perform unforeseen tasks, and thereby encounter new anomaly types. We train and evaluate several state-of-the-art VAD methods, including a novel method specifically adapted for robotic arm manipulation. While the proposed method outperforms many state-of-the-art competitors, all methods remain below a micro-averaged frame-level AUC threshold of 70% in the most challenging evaluation setup, confirming the difficulty of the proposed benchmark. We publicly release our dataset and code at this https URL.