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Towards Learning and Classifying Spatio-Temporal Activities in a Stream Processing Framework
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology. (KPLAB)ORCID iD: 0000-0002-8546-4431
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, The Institute of Technology. (KPLAB)
2014 (English)In: STAIRS 2014: Proceedings of the 7th European Starting AI Researcher Symposium / [ed] Ulle Endriss and João Leite, IOS Press, 2014, 280-289 p.Conference paper, Published paper (Refereed)
Abstract [en]

We propose an unsupervised stream processing framework that learns a Bayesian representation of 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 causal Bayesian graph. This allows the model to be efficient through compactness and sparsity in the causal graph, and to provide probabilities at any level of abstraction for activities or chains of activities. Methods and ideas from a wide range of previous work are combined and interact to provide a uniform way to tackle a variety of common problems related to learning, classifying and predicting activities. We discuss how to use this framework to perform prediction of future activities and to generate events.

Place, publisher, year, edition, pages
IOS Press, 2014. 280-289 p.
Series
Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389, E-ISSN 1879-8314 ; 264
Keyword [en]
activity recognition, machine learning, knowledge representation, situation awareness, knowledge acquisition, unsupervised learning
National Category
Computer Science
Identifiers
URN: urn:nbn:se:liu:diva-110570DOI: 10.3233/978-1-61499-421-3-280ISI: 000350218400029ISBN: 978-1-61499-420-6 (print)ISBN: 978-1-61499-421-3 (print)OAI: oai:DiVA.org:liu-110570DiVA: diva2:746676
Conference
7th European Starting AI Researcher Symposium (STAIRS-2014), August 18-19, 2014, Prague, Czech Republic
Projects
CUASCENIITCADICSELLIIT
Available from: 2014-09-14 Created: 2014-09-14 Last updated: 2017-04-11Bibliographically approved

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Tiger, MattiasHeintz, Fredrik

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