Measure Locally, Reason Globally: Occlusion-sensitive Articulated Pose Estimation

  title={Measure Locally, Reason Globally: Occlusion-sensitive Articulated Pose Estimation},
  author={Leonid Sigal and Michael J. Black},
  journal={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)},
Part-based tree-structured models have been widely used for 2D articulated human pose-estimation. These approaches admit efficient inference algorithms while capturing the important kinematic constraints of the human body as a graphical model. These methods often fail however when multiple body parts fit the same image region resulting in global pose estimates that poorly explain the overall image evidence. Attempts to solve this problem have focused on the use of strong prior models that are… CONTINUE READING
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