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Human pose estimation with multiple mixture parts model based on upper body categories

[+] Author Affiliations
Aichun Zhu, Hichem Snoussi

Université de Technologie de Troyes, ICD-LM2S, UMR STMR CNRS, 12 rue Marie Curie, CS 42060, Troyes 10004, France

Tian Wang

Beihang University, School of Automation Science and Electrical Engineering, No. 37 XueYuan Road, HaiDian District, Beijing 100191, China

Abel Cherouat

Université de Technologie de Troyes, ICD-GAMMA3, UMR STMR CNRS, 12 rue Marie Curie, CS 42060, Troyes 10004, France

J. Electron. Imaging. 24(4), 043021 (Aug 28, 2015). doi:10.1117/1.JEI.24.4.043021
History: Received January 28, 2015; Accepted August 6, 2015
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Abstract.  The problem of human pose estimation in still images is considered. Most previous works predicted the pose directly with either local deformable models or a global mixture representation in the pose space. We argue that this process of pose estimation can be divided into different stages. We propose a new two-stage framework for human pose estimation. In the pre-estimation stage, there are three steps: upper body detection, model category estimation for the upper body, and full model selection for pose estimation. A new method based on pairwise scores of the upper body is proposed for upper body detection. In the estimation stage, we address the problem of a variety of human poses and activities. The upper body-based multiple mixture parts (MMP) model is proposed. This model not only joins different mixture models together, but can also analyze activities with complex kinematic structures. The model is compared with the state-of-the-art. The experimental results demonstrate the effectiveness of the MMP model.

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Citation

Aichun Zhu ; Hichem Snoussi ; Tian Wang and Abel Cherouat
"Human pose estimation with multiple mixture parts model based on upper body categories", J. Electron. Imaging. 24(4), 043021 (Aug 28, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.4.043021


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