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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

A 3D Human Posture Approach for Activity Recognition Based on Depth Camera

verfasst von : Alessandro Manzi, Filippo Cavallo, Paolo Dario

Erschienen in: Computer Vision – ECCV 2016 Workshops

Verlag: Springer International Publishing

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Abstract

Human activity recognition plays an important role in the context of Ambient Assisted Living (AAL), providing useful tools to improve people quality of life. This work presents an activity recognition algorithm based on the extraction of skeleton joints from a depth camera. The system describes an activity using a set of few and basic postures extracted by means of the X-means clustering algorithm. A multi-class Support Vector Machine, trained with the Sequential Minimal Optimization is employed to perform the classification. The system is evaluated on two public datasets for activity recognition which have different skeleton models, the CAD-60 with 15 joints and the TST with 25 joints. The proposed approach achieves precision/recall performances of 99.8 % on CAD-60 and 97.2 %/91.7 % on TST. The results are promising for an applied use in the context of AAL.

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Vorheriges Kapitel Human Joint Angle Estimation and Gesture Recognition for Assistive Robotic Vision
Nächstes Kapitel ISANA: Wearable Context-Aware Indoor Assistive Navigation with Obstacle Avoidance for the Blind
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Metadaten
Titel
A 3D Human Posture Approach for Activity Recognition Based on Depth Camera
verfasst von
Alessandro Manzi
Filippo Cavallo
Paolo Dario
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016 Workshops

Print ISBN: 978-3-319-48880-6

Electronic ISBN: 978-3-319-48881-3

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-48881-3_30