Josef Kittler , University of Surrey, Guildford
William Christmas , University of Surrey, Guildford
Teo de Campos , University of Surrey, Guildford
David Windridge , University of Surrey, Guildford
Fei Yan , University of Surrey, Guildford
John Illingworth , University of Surrey, Guildford
Magda Osman , Queen Mary, University of London, London
We address the problem of anomaly detection in machine perception. The concept of domain anomaly is introduced as distinct from the conventional notion of anomaly used in the literature. We propose a unified framework for anomaly detection which exposes the multifaceted nature of anomalies and suggest effective mechanisms for identifying and distinguishing each facet as instruments for domain anomaly detection. The framework draws on the Bayesian probabilistic reasoning apparatus which clearly defines concepts such as outlier, noise, distribution drift, novelty detection (object, object primitive), rare events, and unexpected events. Based on these concepts we provide a taxonomy of domain anomaly events. One of the mechanisms helping to pinpoint the nature of anomaly is based on detecting incongruence between contextual and noncontextual sensor(y) data interpretation. The proposed methodology has wide applicability. It underpins in a unified way the anomaly detection applications found in the literature. To illustrate some of its distinguishing features, in here the domain anomaly detection methodology is applied to the problem of anomaly detection for a video annotation system.
anomaly detection mechanisms, Domain anomaly, anomaly detection framework, machine perception
J. Kittler et al., "Domain Anomaly Detection in Machine Perception: A System Architecture and Taxonomy," in IEEE Transactions on Pattern Analysis & Machine Intelligence.