Real-Time Body Pose Recognition Using 2D or 3D Haarlets |
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Authors: | Michael Van den Bergh Esther Koller-Meier Luc Van Gool |
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Affiliation: | (1) Computer Vision Laboratory, ETH Zurich, Zurich, Switzerland;(2) ESAT-PSI/VISICS, Katholieke Universiteit Leuven, Leuven, Belgium |
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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. |
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Keywords: | Pose estimation Pose recognition Silhouettes 3D hulls LDA ANMM Haarlets |
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