Anomaly detection plays a critical role in intelligent video surveillance. However, real-world video data obtained always contains large numbers of normal video data, along with large numbers of unlabeled data. A promising solution with one-class classification and semi-supervised learning may not be satisfactory as they fail to make good use of unlabeled data with only normal data available. In this paper, we introduced a new framework, called Positive Unlabeled learning-based Anomaly event Detection (PU-AD), to exploit the weakly-supervised information. To the best of our knowledge, this is the first work that introduces the PU idea and achieves detecting abnormal events with a limited number of partially labeled data. Experiments on real-world surveillance videos show that the proposed method outperforms the existing state-of-the-art methods.

中文翻译：

---

视频中基于正未标记学习的异常检测

异常检测在智能视频监控中起着至关重要的作用。然而，获得的真实视频数据总是包含大量正常视频数据，以及大量未标记的数据。具有一类分类和半监督学习的有前途的解决方案可能并不令人满意，因为它们无法很好地利用只有正常数据可用的未标记数据。在本文中，我们引入了一个新框架，称为基于正未标记学习的异常事件检测 (PU-AD)，以利用弱监督信息。据我们所知，这是第一个引入 PU 思想并用有限数量的部分标记数据实现异常事件检测的工作。对真实世界监控视频的实验表明，所提出的方法优于现有的最先进方法。