liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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
Towards Unsupervised Learning, Classification and Prediction of Activities in a Stream-Based Framework
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering. (KPLAB)ORCID iD: 0000-0002-8546-4431
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
2015 (English)In: Proceedings of the Thirteenth Scandinavian Conference on Artificial Intelligence (SCAI), IOS Press, 2015, Vol. 278, p. 147-156Conference paper, Published paper (Refereed)
Abstract [en]

Learning to recognize common activities such as traffic activities and robot behavior is an important and challenging problem related both to AI and robotics. We propose an unsupervised approach that takes streams of observations of objects as input and learns a probabilistic representation of the observed spatio-temporal activities and their causal relations. The dynamics of the activities are modeled using sparse Gaussian processes and their causal relations using a probabilistic graph. The learned model supports in limited form both estimating the most likely current activity and predicting the most likely future activities.  The framework is evaluated by learning activities in a simulated traffic monitoring application and by learning the flight patterns of an autonomous quadcopter.

Place, publisher, year, edition, pages
IOS Press, 2015. Vol. 278, p. 147-156
Series
Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389, E-ISSN 1879-8314 ; 278
Keywords [en]
Online Unsupervised Learning, Activity Recognition, Situation Awareness
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-121125DOI: 10.3233/978-1-61499-589-0-147ISI: 000455950400017ISBN: 978-1-61499-588-3 (print)OAI: oai:DiVA.org:liu-121125DiVA, id: diva2:852110
Conference
13th Scandinavian Conference on Artificial Intelligence, Halmstad, Sweden, November 2015
Projects
CADICSCENIITCUASCUGSELLIIT
Funder
CUGS (National Graduate School in Computer Science)ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsAvailable from: 2015-09-08 Created: 2015-09-08 Last updated: 2020-06-29

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textlänk till artikeln

Authority records

Tiger, MattiasHeintz, Fredrik

Search in DiVA

By author/editor
Tiger, MattiasHeintz, Fredrik
By organisation
Artificial Intelligence and Integrated Computer SystemsFaculty of Science & Engineering
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 315 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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