  Video anomaly detection is of critical practical importance to a variety of
real applications because it allows human attention to be focused on events
that are likely to be of interest, in spite of an otherwise overwhelming volume
of video. We show that applying self-trained deep ordinal regression to video
anomaly detection overcomes two key limitations of existing methods, namely, 1)
being highly dependent on manually labeled normal training data; and 2)
sub-optimal feature learning. By formulating a surrogate two-class ordinal
regression task we devise an end-to-end trainable video anomaly detection
approach that enables joint representation learning and anomaly scoring without
manually labeled normal/abnormal data. Experiments on eight real-world video
scenes show that our proposed method outperforms state-of-the-art methods that
require no labeled training data by a substantial margin, and enables easy and
accurate localization of the identified anomalies. Furthermore, we demonstrate
that our method offers effective human-in-the-loop anomaly detection which can
be critical in applications where anomalies are rare and the false-negative
cost is high.
