Högskolan i Skövde

his.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Sequential Conformal Anomaly Detection in Trajectories based on Hausdorff Distance
Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Artificial Intelligence Lab (SAIL))
Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. (Skövde Artificial Intelligence Lab (SAIL))ORCID-id: 0000-0001-8884-2154
2011 (svensk)Inngår i: Proceedings of the 14th International Conference on Information Fusion (FUSION 2011), IEEE Computer Society, 2011, s. 153-160Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

Abnormal behaviour may indicate important objects and situations in e.g. surveillance applications. This paper is concerned with algorithms for automated anomaly detection in trajectory data. Based on the theory of Conformal prediction, we propose the Similarity based Nearest Neighbour Conformal Anomaly Detector (SNN-CAD) which is a parameter-light algorithm for on-line learning and anomaly detection with wellcalibrated false alarm rate. The only design parameter in SNN-CAD is the dissimilarity measure. We propose two parameterfree dissimilarity measures based on Hausdorff distance for comparing multi-dimensional trajectories of arbitrary length. One of these measures is appropriate for sequential anomaly detection in incomplete trajectories. The proposed algorithms are evaluated using two public data sets. Results show that high sensitivity to labelled anomalies and low false alarm rate can be achieved without any parameter tuning.

sted, utgiver, år, opplag, sider
IEEE Computer Society, 2011. s. 153-160
Emneord [en]
Anomaly detection, trajectory data, Conformal prediction, Hausdorff distance, automated surveillance
HSV kategori
Forskningsprogram
Teknik
Identifikatorer
URN: urn:nbn:se:his:diva-5687Scopus ID: 2-s2.0-80052522144ISBN: 978-1-4577-0267-9 (tryckt)ISBN: 978-0-9824438-2-8 (digital)OAI: oai:DiVA.org:his-5687DiVA, id: diva2:514042
Konferanse
14th International Conference on Information Fusion, Fusion 2011;Chicago, IL;5 July 2011–8 July 2011
Tilgjengelig fra: 2012-04-04 Laget: 2012-04-04 Sist oppdatert: 2020-04-30bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Scopushttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5977571

Person

Laxhammar, RikardFalkman, Göran

Søk i DiVA

Av forfatter/redaktør
Laxhammar, RikardFalkman, Göran
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric

isbn
urn-nbn
Totalt: 251 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf