Human body posture recognition algorithm for still images

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原文链接

NETL
NSTL
Wiley

外文摘要：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.

外文关键词：

image recognitionimage classificationpose estimationconvolutional neural netsrecognition accuracyposition informationconvolutional neural network structurehuman joint pointstacked hourglass modeldeformable convolutionjoint point informationposture recognition methodmanual featuresstill imageshuman body posture recognition algorithm

作者：

Yu, Naigong、Lv, Jian

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作者单位：

Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China|Beijing Key Lab Computat Intelligence & Intellige, Beijing, Peoples R China

Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China

出版年：

2020

DOI：

10.1049/joe.2019.1146

The Journal of Engineering

ISSN：

年,卷(期)：2020.2020(13)

参考文献量19