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.
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References
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)
Wang, W., Yan, Q., Jin, D.: Object-oriented remote sensing image classification based on GEPSO model. Comput. Sci. 42(5), 51–53, 71 (2015)
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)
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)
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)
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)
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)
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)
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)
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)
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Zou, W., Jin, Z. (2020). Algorithm for Motion Video Based on Basketball Image. In: Xu, Z., Parizi, R., Hammoudeh, M., Loyola-González, O. (eds) Cyber Security Intelligence and Analytics. CSIA 2020. Advances in Intelligent Systems and Computing, vol 1146. Springer, Cham. https://doi.org/10.1007/978-3-030-43306-2_111
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DOI: https://doi.org/10.1007/978-3-030-43306-2_111
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