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)

Preview

PDF - Final accepted version (with author's formatting) Download (3MB) | Preview

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.

Actions (login required)

View Item

Statistics

Downloads

Export as CSVXMLJSON

Activity Overview

292Downloads

312Hits

Additional statistics are available via IRStats2.