Pose Estimation Using Local Adjustment with Mixtures-of-Parts Models (Journal of Fiber Bioengineering and Informatics, 2015)
Articulated pose estimation with mixtures-of-parts decomposes human body into several local component\rtemplates with springs connecting each other. Such a method fails in precisely estimating human pose\respecially due to the defects of tree models when human has the complicated pose of body. To address\rthis problem, we propose pose estimation using local adjustment with mixtures-of-parts models. We can\rachieve the most suitable pose of body by the blending and selecting strategy based on the full score\rand the corresponding attributes of limbs and body. The experiments show that the estimation effect\rof human pose of our method is better than the previous method based on articulated pose estimation\rwith mixtures-of-parts.