  Video anomaly detection (VAD) addresses the problem of automatically finding
anomalous events in video data. The primary data modalities on which current
VAD systems work on are monochrome or RGB images. Using depth data in this
context instead is still hardly explored in spite of depth images being a
popular choice in many other computer vision research areas and the increasing
availability of inexpensive depth camera hardware. We evaluate the application
of existing autoencoder-based methods on depth video and propose how the
advantages of using depth data can be leveraged by integration into the loss
function. Training is done unsupervised using normal sequences without need for
any additional annotations. We show that depth allows easy extraction of
auxiliary information for scene analysis in the form of a foreground mask and
demonstrate its beneficial effect on the anomaly detection performance through
evaluation on a large public dataset, for which we are also the first ones to
present results on.
