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Human body posture recognition algorithm for still images

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Human body posture recognition algorithm for still images

Content Provider	IET Digital Library
Author	Yu, Naigong Lv, Jian
Abstract	Aiming at the low accuracy and poor robustness of the current algorithm based on manual features, this study proposed a posture recognition method combining joint point information with convolutional neural network. The deformable convolution is used in the proposed method to improve the stacked hourglass model, so that it can extract the position of the human joint point accurately. At the same time, the convolutional neural network structure is designed to analyse the position information and confidence of the joint point autonomously, and extract the intrinsic link of the joint point of the human body. Finally, the softmax classifier is used to determine the pose category. Experimental verification has been carried out on the Willow data set. Moreover, the recognition accuracy demonstrates the effectiveness and superiority of the improved method.
Starting Page	322
Ending Page	325
Page Count	4
Volume Number	2020
e-ISSN	20513305
Issue Number	Issue 13, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/joe/2020/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/joe.2019.1146
Journal	The Journal of Engineering
Publisher	The Institution of Engineering and Technology
Publisher Date	2020-01-13
Access Restriction	Open
Rights License	Creative Commons Attribution-Non Commercial-No Derivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/)
Subject Keyword	Computer Vision And Image Processing Technique Convolutional Neural Nets Convolutional Neural Network Structure Deformable Convolution Human Body Posture Recognition Algorithm Human Joint Point Image Classification Image Recognition Joint Point Information Manual Feature Neural Computing Technique Pose Estimation Position Information Posture Recognition Method Recognition Accuracy Stacked Hourglass Model Still Image
Content Type	Text
Resource Type	Article

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