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Online Detection of Anomalous Sub-Trajectories: A Sliding Window Approach based on Conformal Anomaly Detection and Local Outlier Factor
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. (SAIL)
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. (SAIL)ORCID iD: 0000-0001-8884-2154
2012 (English)In: Artificial Intelligence Applications and Innovations: AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Halkidiki, Greece, September 27–30, 2012, Proceedings, Part II / [ed] Lazaros Iliadis, Ilias Maglogiannis, Harris Papadopoulos, Kostas Karatzas, Spyros Sioutas, Springer, 2012, 192-202 p.Conference paper, (Refereed)
Abstract [en]

Automated detection of anomalous trajectories is an important problem in the surveillance domain. Various algorithms based on learning of normal trajectory patterns have been proposed for this problem. Yet, these algorithms suffer from one or more of the following limitations: First, they are essentially designed for offline anomaly detection in databases. Second, they are insensitive to local sub-trajectory anomalies. Third, they involve tuning of many parameters and may suffer from high false alarm rates. The main contribution of this paper is the proposal and discussion of the Sliding Window Local Outlier Conformal Anomaly Detector (SWLO-CAD), which is an algorithm for online detection of local sub-trajectory anomalies. It is an instance of the previously proposed Conformal anomaly detector and, hence, operates online with well-calibrated false alarm rate. Moreover, SWLO-CAD is based on Local outlier factor, which is a previously proposed outlier measure that is sensitive to local anomalies. Thus, SWLO-CAD has a unique set of properties that address the issues above.

Place, publisher, year, edition, pages
Springer, 2012. 192-202 p.
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238 ; 382
National Category
Computer Science
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-6921DOI: 10.1007/978-3-642-33412-2_20Scopus ID: 2-s2.0-84870897781ISBN: 978-3-642-33411-5 ISBN: 978-3-642-33412-2 OAI: oai:DiVA.org:his-6921DiVA: diva2:578058
Conference
8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB; Halkidiki; 27 September 2012–30 September 2012
Available from: 2012-12-17 Created: 2012-12-17 Last updated: 2015-01-19Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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