Volume 8, Issue 2
Pose Estimation Using Local Adjustment with Mixtures-of-Parts Models

Peng Cai, Dehui Kong, Shaofan Wang, Baocai Yin, Xiaogang Ruan & Yi Huo

Journal of Fiber Bioengineering & Informatics, 8 (2015), pp. 249-258.

Published online: 2015-08

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  • Abstract

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

  • Keywords

Articulated Model Mixtures-of-Parts Pose Estimation Local Adjustment Blending and Selecting Strategy

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COPYRIGHT: © Global Science Press

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@Article{JFBI-8-249, author = {}, title = {Pose Estimation Using Local Adjustment with Mixtures-of-Parts Models}, journal = {Journal of Fiber Bioengineering and Informatics}, year = {2015}, volume = {8}, number = {2}, pages = {249--258}, abstract = {Articulated pose estimation with mixtures-of-parts decomposes human body into several local component templates with springs connecting each other. Such a method fails in precisely estimating human pose especially due to the defects of tree models when human has the complicated pose of body. To address this problem, we propose pose estimation using local adjustment with mixtures-of-parts models. We can achieve the most suitable pose of body by the blending and selecting strategy based on the full score and the corresponding attributes of limbs and body. The experiments show that the estimation effect of human pose of our method is better than the previous method based on articulated pose estimation with mixtures-of-parts.}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbim00116}, url = {http://global-sci.org/intro/article_detail/jfbi/4704.html} }
TY - JOUR T1 - Pose Estimation Using Local Adjustment with Mixtures-of-Parts Models JO - Journal of Fiber Bioengineering and Informatics VL - 2 SP - 249 EP - 258 PY - 2015 DA - 2015/08 SN - 8 DO - http://doi.org/10.3993/jfbim00116 UR - https://global-sci.org/intro/article_detail/jfbi/4704.html KW - Articulated Model KW - Mixtures-of-Parts KW - Pose Estimation KW - Local Adjustment KW - Blending and Selecting Strategy AB - Articulated pose estimation with mixtures-of-parts decomposes human body into several local component templates with springs connecting each other. Such a method fails in precisely estimating human pose especially due to the defects of tree models when human has the complicated pose of body. To address this problem, we propose pose estimation using local adjustment with mixtures-of-parts models. We can achieve the most suitable pose of body by the blending and selecting strategy based on the full score and the corresponding attributes of limbs and body. The experiments show that the estimation effect of human pose of our method is better than the previous method based on articulated pose estimation with mixtures-of-parts.
Peng Cai, Dehui Kong, Shaofan Wang, Baocai Yin, Xiaogang Ruan & Yi Huo. (2019). Pose Estimation Using Local Adjustment with Mixtures-of-Parts Models. Journal of Fiber Bioengineering and Informatics. 8 (2). 249-258. doi:10.3993/jfbim00116
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