Alexander Artikis, Matthias Weidlich, et al.
EDBT 2014
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.
Alexander Artikis, Matthias Weidlich, et al.
EDBT 2014
Fearghal O'Donncha, Albert Akhriev, et al.
Big Data 2021
Fearghal O’Donncha, Albert Akhriev, et al.
Aquac Int
Matheus Souza, Wynita M. Griggs, et al.
ITSC 2018