  Video anomaly detection is well investigated in weakly-supervised and
one-class classification (OCC) settings. However, unsupervised video anomaly
detection methods are quite sparse, likely because anomalies are less frequent
in occurrence and usually not well-defined, which when coupled with the absence
of ground truth supervision, could adversely affect the performance of the
learning algorithms. This problem is challenging yet rewarding as it can
completely eradicate the costs of obtaining laborious annotations and enable
such systems to be deployed without human intervention. To this end, we propose
a novel unsupervised Generative Cooperative Learning (GCL) approach for video
anomaly detection that exploits the low frequency of anomalies towards building
a cross-supervision between a generator and a discriminator. In essence, both
networks get trained in a cooperative fashion, thereby allowing unsupervised
learning. We conduct extensive experiments on two large-scale video anomaly
detection datasets, UCF crime, and ShanghaiTech. Consistent improvement over
the existing state-of-the-art unsupervised and OCC methods corroborate the
effectiveness of our approach.
