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Primitive-Based Action Representation and Recognition
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-2965-2953
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0002-5750-9655
2011 (English)In: Advanced Robotics, ISSN 0169-1864, E-ISSN 1568-5535, Vol. 25, no 6-7, 871-891 p.Article in journal (Refereed) Published
Abstract [en]

In robotics, there has been a growing interest in expressing actions as a combination of meaningful subparts commonly called motion primitives. Primitives are analogous to words in a language. Similar to words put together according to the rules of language in a sentence, primitives arranged with certain rules make an action. In this paper we investigate modeling and recognition of arm manipulation actions at different levels of complexity using primitives. Primitives are detected automatically in a sequential manner. Here, we assume no prior knowledge on primitives, but look for correlating segments across various sequences. All actions are then modeled within a single hidden Markov models whose structure is learned incrementally as new data is observed. We also generate an action grammar based on these primitives and thus link signals to symbols.

Place, publisher, year, edition, pages
2011. Vol. 25, no 6-7, 871-891 p.
Keyword [en]
Primitive detection, imitation learning, high-level event, activity modeling
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-34401DOI: 10.1163/016918611X563346ISI: 000290747500010Scopus ID: 2-s2.0-79954472581OAI: oai:DiVA.org:kth-34401DiVA: diva2:420912
Funder
ICT - The Next Generation
Note
QC 20110607Available from: 2011-06-07 Created: 2011-06-07 Last updated: 2017-12-11Bibliographically approved

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Kragic, DanicaKjellström, Hedvig

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CiteExportLink to record
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  • apa
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  • en-US
  • fi-FI
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  • Other locale
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Output format
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