标题:Real-time action recognition based on a modified Deep Belief Network model
作者:Zhang, Haiting ;Zhou, Fengyu ;Zhang, Wei ;Yuan, Xianfeng ;Chen, Zhuming
通讯作者:Zhou, Fengyu
作者机构:[Zhang, Haiting ;Zhou, Fengyu ;Zhang, Wei ;Yuan, Xianfeng ] School of Control Science and Engineering, Shandong University, Jinan, China;[Chen, Zhumin 更多
会议名称:2014 IEEE International Conference on Information and Automation, ICIA 2014
会议日期:28 July 2014 through 30 July 2014
来源:2014 IEEE International Conference on Information and Automation, ICIA 2014
出版年:2014
页码:225-228
DOI:10.1109/ICInfA.2014.6932657
关键词:Action Recognition; Coordinates of Joints; Deep Belief Network; Real-time
摘要:This paper presents a real-time human action recognition method based on a modified Deep Belief Network (DBN) model. To recognize human actions, the positions of human joints are taken into account. Each action is made of a sequence of human joint positions. Since the classic DBN cannot deal with temporal information, the proposed method employs the conditional Restricted Boltzmann Machine (cRBM) to handle the human joint sequence. To verify the effectiveness of the proposed method, two skeletal representation datasets are used for testing. Experimental results show that the proposed method is able to achieve real-time human action recognition, and the recognition accuracy is comparable to state-of-the-arts methods. © 2014 IEEE.
收录类别:EI;SCOPUS
Scopus被引频次:5
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84914168372&doi=10.1109%2fICInfA.2014.6932657&partnerID=40&md5=fdf0d61049a90260ac546ed814218ca5
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