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Anomaly detection in sea traffic - a comparison of the Gaussian Mixture Model and the Kernel Density Estimator
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. Saab Systems, Saab AB, Järfälla, Sweden. (Skövde Artificial Intelligence Lab (SAIL))
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0001-8884-2154
Saab Systems, Saab AB, Järfälla, Sweden.
2009 (English)In: Proceedings of the 12th International Conference on Information Fusion, ISIF , 2009, p. 756-763Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a first attempt to evaluate two previously proposed methods for statistical anomaly detection in sea traffic, namely the Gaussian Mixture Model (GMM) and the adaptive Kernel Density Estimator (KDE). A novel performance measure related to anomaly detection, together with an intermediate performance measure related to normalcy modeling, are proposed and evaluated using recorded AIS data of vessel traffic and

simulated anomalous trajectories. The normalcy modeling evaluation indicates that KDE more accurately captures finer details of normal data. Yet, results from anomaly detection show no significant difference between the two techniques and the performance of both is considered suboptimal. Part of the explanation is that the methods are based on a rather artificial division of data into geographical cells. The paper therefore discusses other clustering approaches based on more informed features of data and more background knowledge regarding the structure and natural classes of the data.

Place, publisher, year, edition, pages
ISIF , 2009. p. 756-763
Keyword [en]
Anomaly detection, sea surveillance, Density estimation, Gaussian Mixturer Model, adaptive Kernel Density Estimation
National Category
Computer Sciences
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-3452ISI: 000273560000098Scopus ID: 2-s2.0-70449334343ISBN: 978-0-9824438-0-4 OAI: oai:DiVA.org:his-3452DiVA, id: diva2:273163
Conference
Fusion 2009 : the 12th International Conference on Information Fusion : Grand Hyatt Seattle, Seattle, Washington, USA, 6-9 July, 2009
Available from: 2009-10-20 Created: 2009-10-20 Last updated: 2018-01-12Bibliographically approved

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Laxhammar, RikardFalkman, Göran

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Citation style
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