his.sePublications
Change search
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
The role of visualization and interaction in maritime anomaly detection
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).ORCID iD: 0000-0003-2900-9335
University of Skövde, The Informatics Research Centre. University of Skövde, School of Humanities and Informatics. University of Skövde, Skövde Artificial Intelligence Lab (SAIL).ORCID iD: 0000-0001-8884-2154
2011 (English)In: Visualization and Data Analysis 2011: Proceedings of SPIE / IS & T Electronic Imaging / [ed] Pak Chung Wong, Jinah Park, Ming C. Hao, Chaomei Chen, Katy Börner, David L. Kao, Jonathan C. Roberts, SPIE - International Society for Optical Engineering, 2011, Article number 78680M, 1-12 p.Conference paper, (Refereed)
Abstract [en]

The surveillance of large sea, air or land areas normally involves the analysis of large volumes of heterogeneous data from multiple sources. Timely detection and identification of anomalous behavior or any threat activity is an important objective for enabling homeland security. While it is worth acknowledging that many existing mining applications support identification of anomalous behavior, autonomous anomaly detection systems for area surveillance are rarely used in the real world. We argue that such capabilities and applications present two critical challenges: (1) they need to provide adequate user support and (2) they need to involve the user in the underlying detection process.

In order to encourage the use of anomaly detection capabilities in surveillance systems, this paper analyzes the challenges that existing anomaly detection and behavioral analysis approaches present regarding their use and maintenance by users. We analyze input parameters, detection process, model representation and outcomes. We discuss the role of visualization and interaction in the anomaly detection process. Practical examples from our current research within the maritime domain illustrate key aspects presented.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2011. Article number 78680M, 1-12 p.
Series
Proceedings of SPIE - The International Society for Optical Engineering, ISSN 0277-786X ; Volume 7868
Keyword [en]
visualization, interaction, anomaly detection, data mining, visual analytics
National Category
Computer and Information Science
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-5179DOI: 10.1117/12.871801ISI: 000293625900021Scopus ID: 2-s2.0-79951916432ISBN: 978-0-81948-405-5 OAI: oai:DiVA.org:his-5179DiVA: diva2:428970
Conference
Visualization and Data Analysis 2011; San Francisco, CA; 24 January 2011 through 25 January 2011
Available from: 2011-07-01 Created: 2011-07-01 Last updated: 2014-05-19Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full textScopushttp://spie.org/Publications/Proceedings/Paper/10.1117/12.871801

Search in DiVA

By author/editor
Riveiro, MariaFalkman, Göran
By organisation
School of Humanities and InformaticsThe Informatics Research CentreSkövde Artificial Intelligence Lab (SAIL)
Computer and Information Science

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 37 hits
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