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Memorias de investigación
Communications at congresses:
Fast human pose estimation using 3D Zernike descriptors
Year:2012
Research Areas
  • Picture synthesis,
  • Shape recognition
Information
Abstract
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.
International
Si
Congress
IS&T/SPIE International Conference on Three-Dimensional Image Processing (3DIP) and Applications
960
Place
Burlingame (CA), USA
Reviewers
Si
ISBN/ISSN
0277-786X
10.1117/12.908963
Start Date
25/01/2012
End Date
26/01/2012
From page
82900
To page
82900
Proceedings of SPIE, vol. 8290
Participants
  • Autor: Daniel Berjon Diez (UPM)
  • Autor: Francisco Moran Burgos (UPM)
Research Group, Departaments and Institutes related
  • Creador: Departamento: Señales, Sistemas y Radiocomunicaciones
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