Fast human pose estimation using 3D Zernike descriptors

Berjón Díez, Daniel y Morán Burgos, Francisco (2012). Fast human pose estimation using 3D Zernike descriptors. En: "Three-Dimensional Image Processing (3DIP) and Applications II", 24/01/2012 - 26/01/2012, Burlingame, California, USA. pp. 1-6. https://doi.org/10.1117/12.908963.

Descripción

Título: Fast human pose estimation using 3D Zernike descriptors
Autor/es:
  • Berjón Díez, Daniel
  • Morán Burgos, Francisco
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: Three-Dimensional Image Processing (3DIP) and Applications II
Fechas del Evento: 24/01/2012 - 26/01/2012
Lugar del Evento: Burlingame, California, USA
Título del Libro: Three-Dimensional Image Processing (3DIP) and Applications II
Fecha: 2012
Volumen: 8290
Materias:
Palabras Clave Informales: Human pose estimation, computer vision, Zernike moments, parallel computing
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

Markerless video-based human pose estimation algorithms face a high-dimensional problem that is frequently broken down into several lower-dimensional ones by estimating the pose of each limb separately. However, in order to do so they need to reliably locate the torso, for which they typically rely on time coherence and tracking algorithms. Their losing track usually results in catastrophic failure of the process, requiring human intervention and thus precluding their usage in real-time applications. We propose a very fast rough pose estimation scheme based on global shape descriptors built on 3D Zernike moments. Using an articulated model that we configure in many poses, a large database of descriptor/pose pairs can be computed off-line. Thus, the only steps that must be done on-line are the extraction of the descriptors for each input volume and a search against the database to get the most likely poses. While the result of such process is not a fine pose estimation, it can be useful to help more sophisticated algorithms to regain track or make more educated guesses when creating new particles in particle-filter-based tracking schemes. We have achieved a performance of about ten fps on a single computer using a database of about one million entries.

Más información

ID de Registro: 30512
Identificador DC: http://oa.upm.es/30512/
Identificador OAI: oai:oa.upm.es:30512
Identificador DOI: 10.1117/12.908963
Depositado por: Memoria Investigacion
Depositado el: 13 Sep 2014 08:32
Ultima Modificación: 22 Sep 2014 11:49
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