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

Michael Van den Bergh, Esther Koller-Meier, Luc Van Gool

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.