基于混合关节肢体模型的深度人体姿态估计方法

doi:10.16451/j.cnki.issn1003-6059.201902001

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Abstract A flexible mixture model is proposed to solve the problems of human pose estimation. The model is composed of joint appearance and inner-joint relationship models, and it is trained through a deep convolutional neural network (DCNN). Firstly, a graphical model is constructed to represent joints and limbs of human body. Secondly, images are decomposed into several image blocks centered on the joints and used as training input data. Finally, a multiple classification DCNN network is obtained to perform human pose estimation.The proposed method is more flexible for human body representation, and the detection rate of joint points and the correct detection rate are effectively improved.

Key words： Deep Learning Deep Convolutional Neural Network Human Pose Estimation Graphical Model

Received: 03 July 2018

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No.61502105,61672159), Technology Guided Project of Fujian Province(No.2017H0015), College-University Cooperation Project of Fujian Province(No.2017H6008)

About author:: (LIU Binghan, master, professor. Her research interests include intelligent video processing and analysis.) (LI Zhenda, master student. His research interests include computer vision.) (KE Xiao(Corresponding author), Ph.D., associate professor. His research interests include computer vision and pattern recognition.)

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Cite this article:

LIU Binghan,LI Zhenda,KE Xiao. Deep Human Pose Estimation Method Based on Mixture Articulated Limb Model[J]. , 2019, 32(2): 97-107.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201902001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I2/97

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