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)

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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.

Item Type: Article
Keywords (uncontrolled): inference mechanisms;object detection;video signal processing;Bayesian probabilistic reasoning apparatus;contextual sensor data interpretation;domain anomaly concept;domain anomaly detection;machine perception;noncontextual sensor data interpretation;video annotation system;Bayes methods;Cognition;Computational modeling;Context;Data models;Detectors;Probabilistic logic;Domain anomaly;anomaly detection framework;anomaly detection mechanisms;machine perception
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 19477
Useful Links:
Depositing User: David Windridge
Date Deposited: 22 Apr 2016 10:20
Last Modified: 09 Jun 2021 17:39
URI: https://eprints.mdx.ac.uk/id/eprint/19477

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