An Improved Human Action Recognition Method Based on 3D Convolutional Neural Network

  • Jingmei Li
  • Zhenxin XuEmail author
  • Jianli Li
  • Jiaxiang Wang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 279)


Aiming at the problems such as complex feature extraction, low recognition rate and low robustness in the traditional human action recognition algorithms, an improved 3D convolutional neural network method for human action recognition is proposed. The network only uses grayscale images and the number of image frames as input. At the same time, two layers of nonlinear convolutional layers are added to the problem of less convolution and convolution kernels in the original network, which not only increases the number of convolution kernels in the network. Quantity, and make the network have better abstraction ability, at the same time in order to prevent the network from appearing the phenomenon of overfitting, the dropout technology was added in the network to regularize. Experiments were performed on the UCF101 data set, achieving an accuracy of 96%. Experimental results show that the improved 3D convolutional neural network model has a higher recognition accuracy in human action recognition.


Human body motion recognition 3D convolutional neural network Dropout 



This work was supported by the National Key Research and Development Plan of China under Grant No. 2016YFB0801004.


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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  • Jingmei Li
    • 1
  • Zhenxin Xu
    • 1
    Email author
  • Jianli Li
    • 1
  • Jiaxiang Wang
    • 1
  1. 1.College of Computer Science and TechnologyHarbin Engineering UniversityHarbinChina

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