Enhanced gesture-based human-computer interaction through a compressive sensing reduction scheme of very large and efficient depth feature descriptors

Mantecón del Valle, Tomás; Mantecón Jené, Ana; Blanco Adán, Carlos Roberto del; Jaureguizar Núñez, Fernando y García Santos, Narciso (2015). Enhanced gesture-based human-computer interaction through a compressive sensing reduction scheme of very large and efficient depth feature descriptors. En: "12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2015)", 25/08/2015 - 28/08/2015, Karlsruhe, Germany. pp. 1-6. https://doi.org/10.1109/AVSS.2015.7301804.

Descripción

Título: Enhanced gesture-based human-computer interaction through a compressive sensing reduction scheme of very large and efficient depth feature descriptors
Autor/es:
  • Mantecón del Valle, Tomás
  • Mantecón Jené, Ana
  • Blanco Adán, Carlos Roberto del
  • Jaureguizar Núñez, Fernando
  • García Santos, Narciso
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2015)
Fechas del Evento: 25/08/2015 - 28/08/2015
Lugar del Evento: Karlsruhe, Germany
Título del Libro: 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS 2015)
Fecha: 2015
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

In this paper, a hand gesture-based recognition system is presented with the aim of recognizing finger-spelling using the American Sign Language. The solution makes use of the depth imagery acquired by the new Kinect 2 sensor that provides more depth resolution. The main novelty is the introduction of a Compressive Sensing step to reduce the dimension of a depth-based feature descriptor, called Depth Spatiograms of Quantized Patterns, which is very discriminative, but also too large for its practical application. The system is composed by three steps: 1) depth-based feature descriptor computation that robustly characterizes the hand gesture; 2) Compressive Sensing based dimensionality reduction that shortens the previous highly discriminative but also large feature vector with almost no information lost; and 3) Support Vector Machine based classification that recognizes the performed hand gestures. Promising recognition results have been obtained in an American Sign Language based database.

Proyectos asociados

TipoCódigoAcrónimoResponsableTítulo
Gobierno de EspañaTEC2010-20412Enhanced 3DTVMinisterio de Economía y CompetitividadSin especificar
Gobierno de EspañaTEC2013-48453MR-UHDTVMinisterio de Economía y CompetitividadSin especificar

Más información

ID de Registro: 42759
Identificador DC: http://oa.upm.es/42759/
Identificador OAI: oai:oa.upm.es:42759
Identificador DOI: 10.1109/AVSS.2015.7301804
Depositado por: Memoria Investigacion
Depositado el: 16 Jul 2016 09:55
Ultima Modificación: 16 Jul 2016 09:55
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