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Multi-task Forest for Human Pose Estimation in Depth Images
2013
2013 International Conference on 3D Vision
In this paper, we address the problem of human body pose estimation from depth data. Previous works based on random forests relied either on a classification strategy to infer the different body parts or on a regression approach to predict directly the joint positions. To permit the inference of very generic poses, those approaches did not consider additional information during the learning phase, e.g. the performed activity. In the present work, we introduce a novel approach to integrate
doi:10.1109/3dv.2013.43
dblp:conf/3dim/LallemandPSTI13
fatcat:33xuzkdvafebfmp7wz4phqduom