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Descripción

In this paper, a novel multi-modal method for person identification in indoor environments is presented. This ap- proach relies on matching the skeletons detected by a Kinect v2 device with wearable devices equipped with inertial sen- sors. Movement features such as yaw and pitch changes are employed to associate a particular Kinect skeleton to a person using the wearable. The entire process of sensor cal- ibration, feature extraction, synchronization and matching is detailed in this work. Six detection scenarios were de- fined to assess the proposed method. Experimental results have shown a high accuracy in the association process.

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Autor(es)

Belmonte Hernández, Alberto -  Solachidis, V. -  Theodoridis, T. -  Hernández Peñaloza, Gustavo Adolfo -  Conti, Giuseppe -  Vretos, N. -  Álvarez García, Federico -  Daras, P. - 

Id.: 71333330

Idioma: eng  - 

Versión: 1.0

Estado: Final

Tipo:  application/pdf - 

Palabras claveRobótica e Informática Industrial - 

Tipo de recurso: info:eu-repo/semantics/conferenceObject  -  Ponencia en Congreso o Jornada  -  PeerReviewed  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

: http://creativecommons.org/licenses/by-nc-nd/3.0/es/

Formatos:  application/pdf - 

Requerimientos técnicos:  Browser: Any - 

Relación: [IsBasedOn] IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2017) | IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2017) | 29/08/2017 - 01/09/2017 | Lecce, Italy
[References] https://ieeexplore.ieee.org/document/8078529/
[References] info:eu-repo/grantAgreement/EC/H2020/ICT4LIFE
[References] info:eu-repo/semantics/altIdentifier/doi/10.1109/AVSS.2017.8078529

Fecha de contribución: 23-may-2018

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