  Motivated by our observation that motion information is the key to good
anomaly detection performance in video, we propose a temporal augmented network
to learn a motion-aware feature. This feature alone can achieve competitive
performance with previous state-of-the-art methods, and when combined with
them, can achieve significant performance improvements. Furthermore, we
incorporate temporal context into the Multiple Instance Learning (MIL) ranking
model by using an attention block. The learned attention weights can help to
differentiate between anomalous and normal video segments better. With the
proposed motion-aware feature and the temporal MIL ranking model, we outperform
previous approaches by a large margin on both anomaly detection and anomalous
action recognition tasks in the UCF Crime dataset.
