Video Anomaly detection in videos refers to the identification of events that do not conform to expected behavior. However, almost all existing methods cast this problem as the minimization of reconstruction errors of training data including only normal events, which may lead to self-reconstruction and cannot guarantee a larger reconstruction error for an abnormal event. In this paper, we propose to formulate the video anomaly detection problem within a regime of video prediction. We advocate that not all video prediction networks are suitable for video anomaly detection. Then, we introduce two principles for the design of a video prediction network for video anomaly detection. Based on them, we elaborately design a video prediction network with appearance and motion constraints for video anomaly detection. Further, to promote the generalization of the prediction-based video anomaly detection for novel scenes, we carefully investigate the usage of a meta learning within our framework, where our model can be fast adapted to a new testing scene with only a few starting frames. Extensive experiments on both a toy dataset and three real datasets validate the effectiveness of our method in terms of robustness to the uncertainty in normal events and the sensitivity to abnormal events.

中文翻译：

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用于视频异常检测的未来帧预测网络


视频中的异常检测是指识别不符合预期行为的事件。然而，几乎所有现有方法都将此问题归结为仅包含正常事件的训练数据的重构误差最小化，这可能导致自重构并且不能保证异常事件具有更大的重构误差。在本文中，我们建议在视频预测范围内制定视频异常检测问题。我们主张并非所有视频预测网络都适合视频异常检测。然后，我们介绍了用于视频异常检测的视频预测网络设计的两个原则。基于它们，我们精心设计了一个具有外观和运动约束的视频预测网络，用于视频异常检测。此外，为了促进针对新场景的基于预测的视频异常检测的泛化，我们仔细研究了框架内元学习的使用，其中我们的模型可以快速适应新的测试场景，只需几个起始帧。对玩具数据集和三个真实数据集的大量实验验证了我们的方法在对正常事件的不确定性的鲁棒性和对异常事件的敏感性方面的有效性。