  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.
