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.

Autore Pugliese

• Di Summa Maria

Tutti gli autori

• Ruta M.; Scioscia F.; Di Summa M.; Ieva S.; Di Sciascio E.; Sacco M.

Titolo volume/Rivista

Lecture notes in computer science

Anno di pubblicazione

2014

ISSN

0302-9743

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

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Numero di citazioni Scopus

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Ultimo Aggiornamento Citazioni

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Settori ERC

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Codici ASJC

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