Anomaly detection in surveillance videos is currently a challenge because of
the diversity of possible events. We propose a deep convolutional neural
network (CNN) that addresses this problem by learning a correspondence between
common object appearances (e.g. pedestrian, background, tree, etc.) and their
associated motions. Our model is designed as a combination of a reconstruction
network and an image translation model that share the same encoder. The former
sub-network determines the most significant structures that appear in video
frames and the latter one attempts to associate motion templates to such
structures. The training stage is performed using only videos of normal events
and the model is then capable to estimate frame-level scores for an unknown
input. The experiments on 6 benchmark datasets demonstrate the competitive
performance of the proposed approach with respect to state-of-the-art methods.

我们提出了一种深度卷积神经网络来检测监控视频中的异常事件，该网络通过学习一种物体外观和运动之间的关系来解决该问题。我们的模型是由重建网络和图像转换模型组成的，它们共享相同的编码器。该模型仅通过正常事件的视频进行训练，可对未知输入进行帧级别的评分。在 6 个基准数据集上的实验证明了该方法在与最先进方法的竞争性能方面的优越性。