  We present a local anomaly detection method in videos. As opposed to most
existing methods that are computationally expensive and are not very
generalizable across different video scenes, we propose an adversarial
framework that learns the temporal local appearance variations by predicting
the appearance of a normally behaving object in the next frame of a scene by
only relying on its current and past appearances. In the presence of an
abnormally behaving object, the reconstruction error between the real and the
predicted next appearance of that object indicates the likelihood of an
anomaly. Our method is competitive with the existing state-of-the-art while
being significantly faster for both training and inference and being better at
generalizing to unseen video scenes.
