Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 400-403.doi: 10.11896/jsjkx.210200079

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Visual Human Action Recognition Based on Deep Belief Network

HONG Yao-qiu   

  1. School of Information Engineering,Jingdezhen University,Jingdezhen,Jiangxi 333000,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:HONG Yao-qiu,born in 1979,associate professor.His main research interests include embedded technology and so on.
  • Supported by:
    National Natural Science Foundation of China(41761012).

Abstract: In order to solve the problem of human action recognition in a large number of videos with complex backgrounds and changing viewpoints on the Internet,an innovative method for human action recognition using unsupervised deep belief networks (DBNs) is proposed.This method uses deep belief networks (DBNs) and restricted Boltzmann machines for unconstrained video action recognition,and uses an unsupervised deep learning model to automatically extract appropriate feature representations without any prior knowledge.Through the identification of a challenging UCF sports data set,the method is proved to be accurate and effective.At the same time,this method is suitable for other visual recognition tasks,and will be extended to unstructured human activity recognition in the future.

Key words: Boltzmann machine, Deep belief networks, Human action recognition, Unsupervised

CLC Number: 

  • TP391
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