Video anomaly detection is usually studied by considering the spatial and temporal contexts. This paper focuses first on spatial context and shows that it can be a fast real-time solution. In the first part of this work there are two main contributions: employing a new deep network for reconstruction and introducing a new regularity scoring function. The new deep architecture is based on pyramid of input images and compared to UNet, the proposed architecture boosts AUC by 15% and the new regularity scoring function is based on SSIM. The second part employs a multiframe approach to distinguish temporal behavior anomalies. The second approach enhances the results by 7% compared to spatial anomaly detection. Comparing the two approaches, if computing power is limited and real time anomaly detection is looked for, single frame detection is preferred while multi frame analysis offers a much wider possibility of anomaly detection.

通常通过考虑空间和时间上下文来研究视频异常检测。本文首先关注空间上下文，并表明它可以是一种快速的实时解决方案。在这项工作的第一部分有两个主要贡献：采用新的深度网络进行重建和引入新的正则性评分函数。新的深度架构基于输入图像的金字塔，与 UNet 相比，所提出的架构将 AUC 提高了 15%，新的正则性评分函数基于 SSIM。第二部分采用多帧方法来区分时间行为异常。与空间异常检测相比，第二种方法将结果提高了 7%。比较这两种方法，如果计算能力有限并且需要实时异常检测，