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A human pose estimation algorithm based on the integration of improved convolutional neural networks and multi-level graph structure constrained model

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Abstract

This work introduces a novel convolutional feature for the task of human pose estimation, which is a framework of fusing a convolutional neural network into a multi-level graph structure model so as to improve the pose estimation results from body-part detection and human spatial structure. In the stage of part detection, the probability vectors corresponding to human body parts in the whole image are analyzed by a convolutional neural network. And then, a novel multi-level graph structure model is designed in this method, which contains the whole body, human body parts and joints, and realizes the coarse-to-fine establishment of a more constrained human spatiTal constraint model from levels of the structures, the edges, to the pixels. The obtained probability vectors are put into the multi-level graph structure model to compute the location coordinates for each joint, successfully achieving a framework of combining the deep learning network and the multi-level graph structure model. A large number of qualitative and quantitative experimental results show that compared with other state-of-art methods, the integration of the deep learning network and the multi-level pictorial structure model can improve the accuracy of human pose estimation to a greater extent.

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Funding

This work was financially supported by the Ministry of Education Project of Humanities and Social Sciences: Research on the Development of High-quality Preschool Physical Education from the Perspective of Body Cognition (No. 19YZC890063).

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  1. Department of physical education, China University of Petroleum (East China), Qingdao, 266580, Shandong, China

    Yang You & Yanmin Zhao

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  1. Yang You
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  2. Yanmin Zhao
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Correspondence to Yang You.

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You, Y., Zhao, Y. A human pose estimation algorithm based on the integration of improved convolutional neural networks and multi-level graph structure constrained model. Pers Ubiquit Comput 23, 607–616 (2019). https://doi.org/10.1007/s00779-019-01255-8

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  • DOI: https://doi.org/10.1007/s00779-019-01255-8

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