Pose Estimation Using Local Adjustment with Mixtures-of-Parts Models

Abstract

Articulated pose estimation with mixtures-of-parts decomposes human body into several local component\r templates with springs connecting each other. Such a method fails in precisely estimating human pose\r especially due to the defects of tree models when human has the complicated pose of body. To address\r this problem, we propose pose estimation using local adjustment with mixtures-of-parts models. We can\r achieve the most suitable pose of body by the blending and selecting strategy based on the full score\r and the corresponding attributes of limbs and body. The experiments show that the estimation effect\r of human pose of our method is better than the previous method based on articulated pose estimation\r with mixtures-of-parts.