Using Richer Models for Articulated Pose Estimation of Footballers
published: Oct. 9, 2012, recorded: September 2012, views: 3861
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We present a fully automatic procedure for reconstructing the pose of a person in 3D from images taken from multiple views. We demonstrate a novel approach for learning more complex models using SVM-Rank, to reorder a set of high scoring configurations. The new model in many cases can resolve the problem of double counting of limbs which happens often in the pictorial structure based models. We address the problem of flipping ambiguity to find the correct correspondences of 2D predictions across all views. We obtain improvements for 2D prediction over the state of art methods on our dataset. We show that the results in many cases are good enough for a fully automatic 3D reconstruction with uncalibrated cameras.
Download slides: bmvc2012_kazemi_pose_estimation_01.pdf (4.0 MB)
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