  Video Anomaly Detection(VAD) has been traditionally tackled in two main
methodologies: the reconstruction-based approach and the prediction-based one.
As the reconstruction-based methods learn to generalize the input image, the
model merely learns an identity function and strongly causes the problem called
generalizing issue. On the other hand, since the prediction-based ones learn to
predict a future frame given several previous frames, they are less sensitive
to the generalizing issue. However, it is still uncertain if the model can
learn the spatio-temporal context of a video. Our intuition is that the
understanding of the spatio-temporal context of a video plays a vital role in
VAD as it provides precise information on how the appearance of an event in a
video clip changes. Hence, to fully exploit the context information for anomaly
detection in video circumstances, we designed the transformer model with three
different contextual prediction streams: masked, whole and partial. By learning
to predict the missing frames of consecutive normal frames, our model can
effectively learn various normality patterns in the video, which leads to a
high reconstruction error at the abnormal cases that are unsuitable to the
learned context. To verify the effectiveness of our approach, we assess our
model on the public benchmark datasets: USCD Pedestrian 2, CUHK Avenue and
ShanghaiTech and evaluate the performance with the anomaly score metric of
reconstruction error. The results demonstrate that our proposed approach
achieves a competitive performance compared to the existing video anomaly
detection methods.
