Domain Anomaly Detection in Machine Perception: A System Architecture and Taxonomy

Abstract

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. 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.

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“…Collectively including all the perspectives, 'coincidences' could be defined as "coincidences are surprising pattern repetitions ["anomalies" [31]] that are observed to be unlikely by chance but are nonetheless ascribed to chance since the search for causal mechanisms has not produced anything more plausible than mere chance" [25]. This could also be understood with the help of the "3 C's Framework for Coincidences."…”

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“…Input [11] herein presents architecture of system basically to detect those anamalies in the respective machine and the domain in perception. Predictor of Bayesian kind is herein improved by notions including outlier, distribution drift, noise and detection of novelty and of rare type events.…”

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“…Other work by Kittler et al [19] present a system architecture to detect anomalies in the machine perception domain. In particular, they propose a set of definitions and taxonomies to clearly define the roles and boundaries within their anomaly detection architecture.…”

“…This approach is in distinct contrast to the work proposed by this paper. Concretely, Kittler et al [19] present context as more analagous to semantics in that the additional information they add is similar to domain ontologies rather than contextual information inherently associated within the data.…”