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| Pose Estimation Using Local Adjustment with Mixtures-of-Parts Models |
| Peng Cai, Dehui Kong, Shaofan Wang, Baocai Yin, Xiaogang Ruan, Yi Huo |
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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.
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| Cite this article: |
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Peng Cai,Dehui Kong,Shaofan Wang, et al. Pose Estimation Using Local Adjustment with Mixtures-of-Parts Models[J]. Journal of Fiber Bioengineering and Informatics, 2015, 8(2): 249-258.
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2015, Vol. 8 
