Body posture recognition as a discovery problem: A semantic-based framework (Contributo in atti di convegno)

Type
Label
  • Body posture recognition as a discovery problem: A semantic-based framework (Contributo in atti di convegno) (literal)
Anno
  • 2014-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-319-09912-5_14 (literal)
Alternative label
  • Ruta M.; Scioscia F.; Di Summa M.; Ieva S.; Di Sciascio E.; Sacco M. (2014)
    Body posture recognition as a discovery problem: A semantic-based framework
    in The 2014 International Conference on Active Media Technology (AMT'14)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Ruta M.; Scioscia F.; Di Summa M.; Ieva S.; Di Sciascio E.; Sacco M. (literal)
Pagina inizio
  • 160 (literal)
Pagina fine
  • 173 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.scopus.com/inward/record.url?eid=2-s2.0-84905404234&partnerID=q2rCbXpz (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 8610 LNCS (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 8610 LNCS (literal)
Rivista
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Politecnico di Bari, Bari, Italy; Politecnico di Bari, Bari, Italy; Consiglio Nazionale Delle Ricerche, Bari, Italy; Politecnico di Bari, Bari, Italy; Politecnico di Bari, Bari, Italy; Consiglio Nazionale Delle Ricerche, Milano, Italy (literal)
Titolo
  • Body posture recognition as a discovery problem: A semantic-based framework (literal)
Abstract
  • The automatic detection of human activities requires large computational resources to increase recognition performances and sophisticated capturing devices to produce accurate results. Anyway, often innovative analysis methods applied to data extracted by off-the-shelf detection peripherals can return acceptable outcomes. In this paper a framework is proposed for automated posture recognition, exploiting depth data provided by a commercial tracking device. The detection problem is handled as a semantic-based resource discovery. A simple yet general data model and a corresponding ontology create the needed terminological substratum for an automatic posture annotation via standard Semantic Web languages. Hence, a logic-based matchmaking allows to compare retrieved annotations with standard posture descriptions stored as individuals in a proper Knowledge Base. Finally, non-standard inferences and a similarity-based ranking support the discovery of the best matching posture. This framework has been implemented in a prototypical tool and preliminary experimental tests have been carried out w.r.t. a reference dataset. © 2014 Springer International Publishing. (literal)
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