Yeonho Kim

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This paper addresses the issues of 3D human pose estimation, tracking and recognition from RGB-D video sequences using a generative structured framework. Most existing approaches focus on these issues using discriminative models. However, a discriminative model has certain drawbacks: a) it requires expensive training steps and large amount of training(More)
This paper proposes a model-based human pose estimation from a sequence of monocular depth images using ridge data and data pruning. The proposed method uses the ridge data that is defined as the local maxima in the distance map because it estimates the human pose robustly and fast due to its selective representation of body skeletons. The proposed method(More)
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