Descripción
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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. | |
Internacional
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Si |
Nombre congreso
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2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) |
Tipo de participación
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960 |
Lugar del congreso
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Lecce (Italia) |
Revisores
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Si |
ISBN o ISSN
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978-1-5386-2939-0 |
DOI
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10.1109/AVSS.2017.8078529 |
Fecha inicio congreso
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29/08/2017 |
Fecha fin congreso
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01/09/2017 |
Desde la página
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416 |
Hasta la página
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421 |
Título de las actas
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2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) |