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Evaluation of Normal Model Visualization for Anomaly Detection in Maritime Traffic
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2900-9335
2014 (English)In: ACM Transactions on Interactive Intelligent Systems (TiiS), ISSN 2160-6455, Vol. 4, no 1, 5Article in journal (Refereed) Published
Abstract [en]

Monitoring dynamic objects in surveillance applications is normally a demanding activity for operators, not only because of the complexity and high dimensionality of the data but also because of other factors like time constraints and uncertainty. Timely detection of anomalous objects or situations that need further investigation may reduce operators' cognitive load. Surveillance applications may include anomaly detection capabilities, but their use is not widespread, since they usually generate a high number of false alarms, they do not provide appropriate cognitive support for operators, and their outcomes can be difficult to comprehend and trust. Visual analytics can bridge the gap between computational and human approaches to detecting anomalous behavior in traffic data, making this process more transparent. As a step toward this goal of transparency, this article presents an evaluation that assesses whether visualizations of normal behavioral models of vessel traffic support two of the main analytical tasks specified during our field work in maritime control centers. The evaluation combines quantitative and qualitative usability assessments. The quantitative evaluation, which was carried out with a proof-of-concept prototype, reveals that participants who used the visualization of normal behavioral models outperformed the group which did not do so. The qualitative assessment shows that domain experts have a positive attitude towards the provision of automatic support and the visualization of normal behavioral models, since these aids may reduce reaction time and increase trust in and comprehensibility of the system

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2014. Vol. 4, no 1, 5
Keyword [en]
evaluation, analytical reasoning, anomaly detection, maritime surveillance
National Category
Computer Science Human Computer Interaction
Research subject
Natural sciences; Technology
Identifiers
URN: urn:nbn:se:his:diva-9047DOI: 10.1145/2591511OAI: oai:DiVA.org:his-9047DiVA: diva2:715265
Available from: 2014-05-02 Created: 2014-05-02 Last updated: 2015-08-18Bibliographically approved

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Publisher's full texthttp://dl.acm.org/citation.cfm?id=2602757.2591511

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf