  Due to the limited availability of anomalous samples for training, video
anomaly detection is commonly viewed as a one-class classification problem.
Many prevalent methods investigate the reconstruction difference produced by
AutoEncoders (AEs) under the assumption that the AEs would reconstruct the
normal data well while reconstructing anomalies poorly. However, even with only
normal data training, the AEs often reconstruct anomalies well, which depletes
their anomaly detection performance. To alleviate this issue, we propose a
simple yet efficient framework for video anomaly detection. The pseudo anomaly
samples are introduced, which are synthesized from only normal data by
embedding random mask tokens without extra data processing. We also propose a
normalcy consistency training strategy that encourages the AEs to better learn
the regular knowledge from normal and corresponding pseudo anomaly data. This
way, the AEs learn more distinct reconstruction boundaries between normal and
abnormal data, resulting in superior anomaly discrimination capability.
Experimental results demonstrate the effectiveness of the proposed method.
