发表机构
ICI Bucharest; University of Bucharest（布加勒斯特国家信息研究与发展研究所; 布加勒斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
RoboVAD是一个大规模跨域视频异常检测基准，针对机械臂操作视频，包含训练中未见动作和异常类型的挑战性场景；评估显示现有方法性能有限，但新方法优于竞争对手。
AI 中文摘要
视频异常检测（VAD）是一项被积极研究的任务，在公共监控和道路交通安全等典型场景中具有广泛的应用。该任务同样与机械臂交互相关，在交互中具有若干下游应用，包括学习更好的交互和操作能力、在异常发生时触发恢复程序等。尽管具有相关性，机械臂操作视频中的异常检测探索受限于可用资源的数量较少。为此，我们引入了RoboVAD，一个用于视频异常检测的大规模基准，包含具有挑战性的跨域评估场景，其中某些动作（由机械臂执行的任务）和异常类型（执行某些任务时发生的错误）在训练期间保持不可见。RoboVAD旨在真实场景中对VAD方法进行基准测试，在这些场景中，机械臂可以执行不可预见的任务，从而遇到新的异常类型。我们训练并评估了几种最先进的VAD方法，包括一种专门针对机械臂操作进行适配的新方法。尽管所提出的方法优于许多最先进的竞争对手，但在最具挑战性的评估设置中，所有方法的微平均帧级AUC阈值仍低于70%，这证实了所提出基准的难度。我们在该https URL上公开发布了我们的数据集和代码。
英文摘要
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 https://zenodo.org/records/22754659.
