Skip to main content
Springer Nature Link
Log in

A football training method based on improved tiny-yolov3 and virtual reality

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

The traditional way of football training could be subject to some factors such as the field, and the problems and scenes in the training can not be recovered fully. Thus, this paper proposes a football training method based on the improved tiny-yolovs3 model and the virtual reality (VR). Firstly, the paper makes use of the improved tiny-yolov3 model to detect the football in motion. Then, the paper uses the binocular camera to get the coordinates of the football. Finally, the paper reproduces the position of football in the virtual reality environment. The experimental results show that the football training method is feasible.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Banks J, Corke P (2001) Quantitative evaluation of matching methods and validity measures for stereo vision. Int J Robot Res 20(7):512–532

    Article  Google Scholar 

  2. Everingham M, Winn J (2006) The PASCAL visual object classes challenge 2007(VOC2007) development kit. Int J Comput Vis 111(1):98–136

    Article  Google Scholar 

  3. Fang D, Nayga RM, West GH, Bazzani C, Snell HA (2020) On the use of virtual reality in mitigating hypothetical bias in choice experiments. Am J Agricult Econ 103(1):142–161

    Article  Google Scholar 

  4. Fransson PA, Patel M, Jensen H, Lundberg M, Tjernstrom F, Magnusson M, Hansson EE (2019) Postural instability in an immersive virtual reality adapts with repetition and includes directional and gender specific effects. Sci Reports 9

  5. Gazda J, Florek M (2012) Economic and non-economic effects of hosting mega sport events. Poland and the uefa euro 2012 case study. Actual Probl Econ 133:32–39

    Google Scholar 

  6. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. Proc IEEE Conf CVPR:580–587

  7. Hartigan JA, Wong MA (1979) Algorithm AS 136: a K-means clustering algorithm. J Roy Stat Soc C, Appl Statist 28(1):100–108

    MATH  Google Scholar 

  8. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proc IEEE Conf CVPR:770–778

  9. Huang G, Liu Z, Laurens VDM, Weinberger KQ (2017) Densely connected convolutional networks, in proc. IEEE Conf, CVPR

    Google Scholar 

  10. Kohavi B, Beato M, Laver L, Freitas TT, Chung LH, Dello Iacono (2020) Effectiveness of field-based resistance training protocols on hip muscle strength among young elite football players. Clin J Sport Med 30(5):470–477

    Google Scholar 

  11. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: single shot multibox detector. Proc. Eur. Conf. Comput. Vis. Cham, Switzerland: Springer, pp. 21–37.

  12. Mcgrane A, Bird N, Arten C, O'Sullivan K (2020) “All my problems go way for 90 minutes”: how football and psychotherapy improves young Men’s mental health. Am J Mens Health 14(5)

  13. Propheter G (2019) Professional sports as economic activity magnets: some evidence from employment microdata. J Urban Aff 61(6):842–852

    Article  Google Scholar 

  14. Redmon J, Farhadi A, YOLOv3: An incremental improvement, 2018, arXiv: 1804. 02767. [Online]. Available: https://arxiv.org/abs/1804.02767

  15. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. Proc IEEE Conf Comput Vis Pattern Recognit:779–788

  16. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Patt Anal Mach Intell 39(6):1137–1149

    Article  Google Scholar 

  17. Tarzi C, Aubrey J, Rotundo M, Armstrong N (2020) MD. Cusimano, professional assessment of potential concussions in elite football tournaments. Injury Preven 26(6):536–539

    Article  Google Scholar 

  18. Wu XJ, Chen HM, Wu XL, Wu SJ, Huang JB (2020) Simultaneous localization and mapping of medical burn areas based on binocular vision and capsule networks. Soft Comput 24(23):18155–18171

    Article  Google Scholar 

  19. Yin HP, Chen B, Chai Y, Liul ZD (2016) Vision-based object detection and tracking: a review. Acta Automat Sin 42(10):1466–1489

    MATH  Google Scholar 

  20. Yu Y, Mo Y (2017) Deep learning principles and tensorflow practice. Publishing House Electron. Ind, Beijing, pp 88–98

    Google Scholar 

  21. Zhang ZY (1999) Flexible camera calibration by viewing a plane from unknown orientations, Seventh IEEE Int Conf Comput Vision

Download references

Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Dong Xiao, Jinbo Niu & Jian Feng

  2. Liaoning Key Laboratory of Intelligent Diagnosis and Safety for Metallurgical Industry, Northeastern University, Shenyang, 110819, China

    Dong Xiao

Authors
  1. Dong Xiao
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jinbo Niu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Jian Feng
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Dong Xiao.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, D., Niu, J. & Feng, J. A football training method based on improved tiny-yolov3 and virtual reality. Multimed Tools Appl 81, 14283–14301 (2022). https://doi.org/10.1007/s11042-022-12404-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12404-2

Keywords