Real-Time Human Pose Estimation from Body-Scanned Point Clouds

Abstract : This paper presents a novel approach to estimate the human pose from a body-scanned point cloud. To do so, a predefined skeleton model is first initialized according to both the skeleton base point and its torso limb obtained by Principal Component Analysis (PCA). Then, the body parts are iteratively clustered and the skeleton limb fitting is performed, based on Expectation Maximization (EM). The human pose is given by the location of each skeletal node in the fitted skeleton model. Experimental results show the ability of the method to estimate the human pose from multiple point cloud video sequences representing the external surface of a scanned human body; being robust, precise and handling large portions of missing data due to occlusions, acquisition hindrances or registration inaccuracies.
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Communication dans un congrès
International Conference on Computer Vision Theory and Applications, Mar 2015, Berlin, Germany. 2014, VISAPP
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Jilliam María Diaz Barros, Frederic Garcia, Désiré Sidibé. Real-Time Human Pose Estimation from Body-Scanned Point Clouds. International Conference on Computer Vision Theory and Applications, Mar 2015, Berlin, Germany. 2014, VISAPP. 〈hal-01145637〉

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