Advertisement

Algorithm for Motion Video Based on Basketball Image

  • Wei ZouEmail author
  • Zhixiang Jin
Conference paper
  • 12 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1146)

Abstract

To enhance quantitative evaluation of basketball motion, basketball video and image analysis are carried out in basketball training, and a feature extraction algorithm for basketball motion video based on edge contour gray detection is proposed. The image noise of the original basketball video frame is reduced by wavelet denoising method. The gray histogram analysis and edge contour extraction are carried out. Combined with the distribution of the adjacent frames of the basketball image, the electronic image stabilization compensation is carried out. According to the results of the electronic image stabilization of the basketball video, the fast capture and feature extraction of the basketball motion action are carried out, and the accurate analysis of the motion video is realized. The proposed algorithm can achieve high frame extraction accuracy of basketball video analysis and enhance recognition of basketball motion.

Keywords

Basketball Image Basketball motion video Electronic image stabilization 

References

  1. 1.
    Wang, X.Y., Zhan, Y.Z.: A watermarking scheme for three-dimensional models by constructing vertex distribution on characteristics. J. Comput.-Aided Des. Comput. Graph. 26(2), 272–279 (2014)Google Scholar
  2. 2.
    Wang, W., Yan, Q., Jin, D.: Object-oriented remote sensing image classification based on GEPSO model. Comput. Sci. 42(5), 51–53, 71 (2015)Google Scholar
  3. 3.
    Bian, L., Huo, G., Li, Q.: Multi-threshold MRI image segmentation algorithm based on Curevelet transformation and multi-objective particle swarm optimization. J. Comput. Appl. 36(11), 3188–3195 (2016)Google Scholar
  4. 4.
    Li, J.Y., Dang, J.W., Wang, Y.P.: Medical image segmentation algorithm based on quantum clonal evolution and two-dimensional Tsallis entropy. J. Comput.-Aided Des. Comput. Graph. 26(3), 465–471 (2014)Google Scholar
  5. 5.
    Ortiz, A., Gorriz, J.M., Ramirez, J., et al.: Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering. Inf. Sci. 262(3), 117–136 (2014)CrossRefGoogle Scholar
  6. 6.
    Yu, T., Hu, B., Gao, X., et al.: Research on dynamic tracking and compensation method for hyperspectral interference imaging. Acta Photonica Sinica 45(7), 0710003 (2016)Google Scholar
  7. 7.
    Cheung, M.H., Southwell, R., Hou, F., et al.: Distributed time-sensitive task selection in mobile crowdsensing. In: Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 157–166. ACM, New York (2015)Google Scholar
  8. 8.
    Rui, L.L., Zhang, P., Huang, H.Q., et al.: Reputation-based incentive mechanisms in crowdsourcing. J. Electron. Inf. Technol. 38(7), 1808–1815 (2016)Google Scholar
  9. 9.
    Zhang, Y., Jiang, C., Song, L., et al.: Incentive mechanism for mobile crowdsourcing using an optimized tournament model. IEEE J. Sel. Areas Commun. 35(4), 880–892 (2017)CrossRefGoogle Scholar
  10. 10.
    Jiang, T.T., Xiao, W.D., Zhang, C., et al.: Text visualization method for time series based on Sankey diagram. Appl. Res. Comput. 33(9), 2683–2687 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Sports DepartmentHuazhong Agricultural UniversityWuhanChina

Personalised recommendations