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.

A 3D Human Posture Approach for Activity Recognition Based on Depth Camera / MANZI, Alessandro; CAVALLO, Filippo; DARIO, Paolo. - ELETTRONICO. - 9914:(2016), pp. 432-447. ((Intervento presentato al convegno 14th European Conference on Computer Vision, ECCV 2016 tenutosi a Amsterdam; Netherlands nel 8 October 2016 through 16 October 2016 [10.1007/978-3-319-48881-3_30].

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

CAVALLO, Filippo;
2016

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.
Computer Vision – ECCV 2016 Workshops
14th European Conference on Computer Vision, ECCV 2016
Amsterdam; Netherlands
8 October 2016 through 16 October 2016
MANZI, Alessandro; CAVALLO, Filippo; DARIO, Paolo
File in questo prodotto:
File Dimensione Formato  
C026 - 3D human posture.pdf

Accesso chiuso

Dimensione 303.93 kB
Formato Adobe PDF
303.93 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/1210833
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 8
  • ???jsp.display-item.citation.isi??? 7
social impact