Domain anomaly detection in machine perception: a system architecture and taxonomy

Kittler, Josef, Christmas, William, De Campos, Teofilo, Windridge, David ORCID: https://orcid.org/0000-0001-5507-8516, Yan, Fei, Illingworth, J. and Osman, M. (2014) Domain anomaly detection in machine perception: a system architecture and taxonomy. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 36 (5) . pp. 845-859. ISSN 0162-8828 [Article] (doi:10.1109/TPAMI.2013.209)

Abstract

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 multifacetted 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.

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