Court-Divisional Team Motion and Player Performance Curve Based Automatic Game Strategy Data Acquisition for Volleyball Analysis

Author:

CHENG Xina1,IKENAGA Takeshi1

Affiliation:

1. Graduate School of Information, Product and Systems, Waseda University

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Signal Processing

Reference15 articles.

1. [1] T.B. Moeslund, G. Thomas, and A. Hilton, Computer Vision in Sports, Springer, 2015. 10.1007/978-3-319-09396-3

2. [2] H.C. Shih, “A survey on content-aware video analysis for sports,” IEEE Trans. Circuits Syst. Video Technol., vol.28, no.5, pp.1212-1231, 2018. 10.1109/tcsvt.2017.2655624

3. [3] http://www.dataproject.com/products/en/en/volleyball/datavolley4

4. [4] H.T. Chen, W.J. Tsai, S.Y. Lee, and J.Y. Yu, “Ball tracking and 3d trajectory approximation with applications to tactics analysis from single-camera volleyball sequences,” Multimed. Tools and Appl., vol.60, no.3, pp.641-667, 2012. 10.1007/s11042-011-0833-y

5. [5] S. Huang, X. Zhuang, X. Cheng, N. Ikoma, M. Honda, and T. Ikenaga, “Player feature based multi-likelihood and spatial relationship based multi-view elimination with least square fitting prediction for volleyball players tracking in 3D space,” IIEEJ Trans. image electronics and visual computing, vol.4, no.2, pp.145-155, 2016.

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1. Video Aided Analysis System of Volleyball Match Based on Artificial Neural Network;2022 International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS);2022-07

2. Multi-physical and Temporal Feature Based Self-correcting Approximation Model for Monocular 3D Volleyball Trajectory Analysis;2021 17th International Conference on Machine Vision and Applications (MVA);2021-07-25

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