  Many real-world monitoring and surveillance applications require non-trivial
anomaly detection to be run in the streaming model. We consider an
incremental-learning approach, wherein a deep-autoencoding (DAE) model of what
is normal is trained and used to detect anomalies at the same time. In the
detection of anomalies, we utilise a novel thresholding mechanism, based on
value at risk (VaR). We compare the resulting convolutional neural network
(CNN) against a number of subspace methods, and present results on
changedetection net.
