Real-Time Body Pose Recognition Using 2D or 3D Haarlets

  • Authors:
  • Michael Bergh;Esther Koller-Meier;Luc Gool

  • Affiliations:
  • Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland;ESAT-PSI/VISICS, Katholieke Universiteit Leuven, Leuven, Belgium;Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland and ESAT-PSI/VISICS, Katholieke Universiteit Leuven, Leuven, Belgium

  • Venue:
  • International Journal of Computer Vision
  • Year:
  • 2009

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Abstract

This article presents a novel approach to markerless real-time pose recognition in a multicamera setup. Body pose is retrieved using example-based classification based on Haar wavelet-like features to allow for real-time pose recognition. Average Neighborhood Margin Maximization (ANMM) is introduced as a powerful new technique to train Haar-like features. The rotation invariant approach is implemented for both 2D classification based on silhouettes, and 3D classification based on visual hulls.