We address the problem of anomaly detection in videos. The goal is to identify unusual behaviours automatically by learning exclusively from normal videos. Most existing approaches are usually data-hungry and have limited generalization abilities. They usually need to be trained on a large number of videos from a target scene to achieve good results in that scene. In this paper, we propose a novel few-shot scene-adaptive anomaly detection problem to address the limitations of previous approaches. Our goal is to learn to detect anomalies in a previously unseen scene with only a few frames. A reliable solution for this new problem will have huge potential in real-world applications since it is expensive to collect a massive amount of data for each target scene. We propose a meta-learning based approach for solving this new problem; extensive experimental results demonstrate the effectiveness of our proposed method.

翻译：我们的目标是通过只从普通视频中学习,自动识别异常行为。大多数现有方法通常是数据饥饿,而且一般化能力有限。它们通常需要接受来自目标场景的大量视频培训,才能在该场景中取得良好结果。在本文中,我们提出一个新颖的微小的场景适应性异常检测问题,以解决以往方法的局限性。我们的目标是学习在先前看不见的场景中发现异常现象,只有几个框架。这个新问题的可靠解决方案在现实世界应用中具有巨大的潜力,因为为每个目标场景收集大量数据非常昂贵。我们提出了一种基于元学习的方法来解决这个新问题;广泛的实验结果显示了我们拟议方法的有效性。