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IJMLC 2020 Vol.10(4): 556-561 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.4.972

Racket Motion Recognition Method Based on Improved Two-Stream Convolution Network

Zhao Hui-Qun and Ye Wei

Abstract—Sports video detection and analysis is a difficult problem in the automatic acquisition of technical and tactical, and also a key issue in whether the collection work can be carried out quickly and efficiently. In this paper, a two-stream deep convolution network model for racket motion recognition and a set of algorithms for racket motion recognition are proposed. The proposed model uses the basic network structure combining Batch Normalization (BN) and Inception network. The idea of batch normalization transformation is used in the recognition of the racket motion, the mini-batch normalization of the two-stream convolution network input is realized. This operation can speed up the training of the network model. The video detection and analysis algorithm based on the above model is given. The algorithm uses RGB image as the input of the spatial network, and the optical flow field image is used as the input of the temporal network. The SVM model is used to fuse the spatio-temporal network to obtain the final detection result. Based on the UCF101 standard data set, both the table tennis and badminton two action video were tested, and the accuracy of 66.92 was obtained. Experiments were conducted on the table tennis and badminton data of the UCF101 standard data set, achieving an accuracy of 66.92%.

Index Terms—Racket motion recognition, batch normalization, convolution network, deep learning.

Huiqun Zhao was with Computer School, North China University of Technology, Beijing 100144 China (e-mail: zhaohq6625@sina.com.
Wei Ye was with College of Computer, North China University of Technology, Beijing, 100144 China (e-mail: 1075388254@qq.com).


Cite: Zhao Hui-Qun and Ye Wei, "Racket Motion Recognition Method Based on Improved Two-Stream Convolution Network," International Journal of Machine Learning and Computing vol. 10, no. 4, pp. 556-561, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijmlc@ejournal.net

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