  Video anomaly detection (VAD) is an important but challenging task in
computer vision. The main challenge rises due to the rarity of training samples
to model all anomaly cases. Hence, semi-supervised anomaly detection methods
have gotten more attention, since they focus on modeling normals and they
detect anomalies by measuring the deviations from normal patterns. Despite
impressive advances of these methods in modeling normal motion and appearance,
long-term motion modeling has not been effectively explored so far. Inspired by
the abilities of the future frame prediction proxy-task, we introduce the task
of future video prediction from a single frame, as a novel proxy-task for video
anomaly detection. This proxy-task alleviates the challenges of previous
methods in learning longer motion patterns. Moreover, we replace the initial
and future raw frames with their corresponding semantic segmentation map, which
not only makes the method aware of object class but also makes the prediction
task less complex for the model. Extensive experiments on the benchmark
datasets (ShanghaiTech, UCSD-Ped1, and UCSD-Ped2) show the effectiveness of the
method and the superiority of its performance compared to SOTA prediction-based
VAD methods.
