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Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy

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Book cover Statistical Learning and Data Sciences (SLDS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9047))

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Abstract

The paper investigates the problem of anomaly detection in the maritime trajectory surveillance domain. Conformal predictors in this paper are used as a basis for anomaly detection. A multi-class hierarchy framework is presented for different class representations. Experiments are conducted with data taken from shipping vessel trajectories using data obtained through AIS (Automatic Identification System) broadcasts and the results are discussed.

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References

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Author information

Authors and Affiliations

  1. Computer Learning Research Center, Royal Holloway University of London, Egham, UK

    James Smith, Ilia Nouretdinov & Alexander Gammerman

  2. Thales UK, London, UK

    Rachel Craddock & Charles Offer

Authors
  1. James Smith
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  2. Ilia Nouretdinov
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  3. Rachel Craddock
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  4. Charles Offer
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  5. Alexander Gammerman
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Corresponding author

Correspondence to Ilia Nouretdinov .

Editor information

Editors and Affiliations

  1. University of London, Egham, Surrey, United Kingdom

    Alexander Gammerman

  2. University of London, Egham, Surrey, United Kingdom

    Vladimir Vovk

  3. Frederick University, Nicosia, Cyprus

    Harris Papadopoulos

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© 2015 Springer International Publishing Switzerland

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Smith, J., Nouretdinov, I., Craddock, R., Offer, C., Gammerman, A. (2015). Conformal Anomaly Detection of Trajectories with a Multi-class Hierarchy. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds) Statistical Learning and Data Sciences. SLDS 2015. Lecture Notes in Computer Science(), vol 9047. Springer, Cham. https://doi.org/10.1007/978-3-319-17091-6_23

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  • DOI: https://doi.org/10.1007/978-3-319-17091-6_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17090-9

  • Online ISBN: 978-3-319-17091-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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