  Video anomaly detection has gained significant attention due to the
increasing requirements of automatic monitoring for surveillance videos.
Especially, the prediction based approach is one of the most studied methods to
detect anomalies by predicting frames that include abnormal events in the test
set after learning with the normal frames of the training set. However, a lot
of prediction networks are computationally expensive owing to the use of
pre-trained optical flow networks, or fail to detect abnormal situations
because of their strong generative ability to predict even the anomalies. To
address these shortcomings, we propose spatial rotation transformation (SRT)
and temporal mixing transformation (TMT) to generate irregular patch cuboids
within normal frame cuboids in order to enhance the learning of normal
features. Additionally, the proposed patch transformation is used only during
the training phase, allowing our model to detect abnormal frames at fast speed
during inference. Our model is evaluated on three anomaly detection benchmarks,
achieving competitive accuracy and surpassing all the previous works in terms
of speed.
