Abstract
The demand of sports video recognition simulation is increasing, but the traditional methods have some limitations in dealing with optical problems. Therefore, the purpose of this paper is to improve the effect of image optical processing by using convolutional neural networks. This paper first analyzes the structure of convolutional neural networks commonly used in computer vision applications, and discusses the improved method of convolutional neural networks to better understand and represent human motion in motion videos. Based on the process analysis of sports video recognition results, the concrete steps of image optical processing are completed. The advantages of convolutional neural network in image optical processing are demonstrated by simulation experiments on some sports videos and comparison with traditional methods. The experimental results show that the image optical processing based on convolutional neural network has a high recognition rate and can be used as an effective auxiliary means for sports training. By accurately analyzing and understanding human movements in sports videos, coaches and trainers can provide more effective training programs tailored to the needs of individual athletes. This can lead to improved performance results and better overall results.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
The data will be available upon request.
References
Bryan, J.A.: Investigating the conservation of mechanical energy using video analysis: four cases. Phys. Educ. 45(1), 50-57 (2010)
Cao, F., Fullwood, M.J.: Inflated performance measures in enhancer–promoter interaction-prediction methods. Nat. Genet. 51(8), 1196–1198 (2019)
Cao, Y., Guan, D., Wu, Y., et al.: Box-level segmentation supervised deep neural networks for accurate and real-time multispectral pedestrian detection. ISPRS J. Photogramm. Remote Sens. 150, 70–79 (2019)
Charte, D., Charte, F., García, S., et al.: A practical tutorial on autoencoders for nonlinear feature fusion: taxonomy, models, software and guidelines. Inform. Fusion 44, 78–96 (2018)
Chen, Z., Ye, S., Chu, X., et al.: Augmenting sports videos with viscommentator. IEEE Trans. Vis. Comput. Graph. 28(1), 824–834 (2021)
Derry, S.J., Pea, R.D., Barron, B., et al.: Conducting video research in the learning sciences: guidance on selection, analysis, technology, and ethics. J. Learn. Sci. 19(1), 3–53 (2010)
Fowler, J.E., Mun, S., Tramel, E.W.: Block-based compressed sensing of images and video. Found. Trends Signal Process. 4(4), 297–416 (2012)
Gao, L., Chen, P.Y., Yu, S.: Demonstration of convolution kernel operation on resistive cross-point array. IEEE Electron Device Lett. 37(7), 870–873 (2016)
Gu, J., Wang, Z., Kuen, J., et al.: Recent advances in convolutional neural networks. Pattern Recogn. 77, 354–377 (2018)
Liu, J., Yang, J., Liu, H., et al.: An improved ant colony algorithm for robot path planning. Soft. Comput. 21(19), 5829–5839 (2017)
Qing, Y., Liu, W.: Hyperspectral image classification based on multi-scale residual network with attention mechanism. Remote Sens. 13(3), 335-339 (2021)
Reardon, C., Tobin, D.P., Tierney, P., et al.: Collision count in rugby union: A comparison of micro-technology and video analysis methods. J. Sports Sci. 35(20), 2028–2034 (2017)
Sato, T., Ito, T., Hirano, T., et al.: Low back pain in childhood and adolescence: Assessment of sports activities. Eur. Spine J. 20(1), 94–99 (2011)
Starman, J.S., Gettys, F.K., Capo, J.A., et al.: Quality and content of internet-based information for ten common orthopaedic sports medicine diagnoses. JBJS. 92(7), 1612–1618 (2010)
Weinstein, B.G.: A computer vision for animal ecology. J. Anim. Ecol. 87(3), 533–545 (2018)
Ying, X., Wang, L., Wang, Y., et al.: Deformable 3d convolution for video super-resolution. IEEE. Signal Process. Lett. 27, 1500–1504 (2020)
Zamani, A.R., Zou, M., Diaz-Montes, J., et al.: Deadline constrained video analysis via in-transit computational environments. IEEE Trans. Serv. Comput. 13(1), 59–72 (2017)
Funding
The paper was supported by Research on the new mode of Venue operation of Summer Asian Cup from the perspective of Sports and education integration with Chinese characteristics, Project Number: L21BTY002.
Author information
Authors and Affiliations
Contributions
YQ has done the first version, KJ and XC has done the simulations. All authors have contributed to the paper’s analysis, discussion, writing, and revision.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interests.
Ethical approval
Not applicable.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Qiao, Y., Jin, K. & Chang, X. Image optical processing based on convolutional neural networks in sports video recognition simulation. Opt Quant Electron 56, 477 (2024). https://doi.org/10.1007/s11082-023-06149-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11082-023-06149-x