Abstract

The paper presents an active vision system for human posture recognition, which is an important function of any assisted living system, suitable to be employed in indoor environments. Both hardware and software architectures are defined in order to meet constraints typically imposed by AAL (Ambient Assisted Living) contexts such as compactness, low-power consumption, installation simplicity, privacy preserving and non-intrusiveness. Two different approaches for feature extraction (topological and volumetric) are discussed and the related discrimination capabilities evaluated by using a statistical learning methodology. Experimental results show the soundness of the presented active vision-based solution in order to classify four main human postures (standing, sitting, bent, lying) with an adequate detail level for the specific AAL application. © 2011 IEEE.

Autore Pugliese

• Leone Alessandro , Diraco Giovanni , Siciliano Pietro Aleardo

Tutti gli autori

• Leone A.; Diraco G.; Siciliano P.

Titolo volume/Rivista

Non Disponibile

Anno di pubblicazione

2011

ISSN

Non Disponibile

ISBN

9781457706226

Numero di citazioni Wos

Nessuna citazione

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Numero di citazioni Scopus

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Settori ERC

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Codici ASJC

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