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Real-time Body Pose Recognition using 2D or 3D Haarlets

Michael Van den Bergh, Esther Koller-Meier, Luc Van Gool
International Journal of Computer Vision
Vol. 83, pp. 72-84, June 2009

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.


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@Article{eth_biwi_00615,
  author = {Michael Van den Bergh and Esther Koller-Meier and Luc Van Gool},
  title = {Real-time Body Pose Recognition using 2D or 3D Haarlets},
  journal = {International Journal of Computer Vision},
  year = {2009},
  month = {June},
  pages = {72-84},
  volume = {83},
  number = {},
  keywords = {Pose Estimation, Markerless, Real-Time, Visual Hull, 3D Haar-like features}
}