“Visualization of putting trajectories in live golf broadcasting”

  • ©Masaki Takahashi, Takahito Ito, Hidehiko Okubo, and Hideki Mitsumine

  • ©Masaki Takahashi, Takahito Ito, Hidehiko Okubo, and Hideki Mitsumine

  • ©Masaki Takahashi, Takahito Ito, Hidehiko Okubo, and Hideki Mitsumine

  • ©Masaki Takahashi, Takahito Ito, Hidehiko Okubo, and Hideki Mitsumine

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Entry Number: 37

Title:

    Visualization of putting trajectories in live golf broadcasting

Presenter(s)/Author(s):



Abstract:


    We developed a system for visualizing golf putting trajectories that can be used in live broadcasting. The trajectory computer graphics (CGs) in a golf putting scene are useful for visualizing the results of past plays and the shape of the green. In addition, displaying past trajectories that were shot near the position of the next player helps TV viewers predict the ball movement of the next play. Visualizing the putting trajectories in this way offers TV viewers a new style of watching live golf tournaments and helps make the programs more understandable and exiting.

References:


    B. Babenko, M. H. Yang, and S. Belongie. 2011. Robust object tracking with online multiple instance learning. In IEEE Trans. Pattern Anal. Mach. Intell. Vol. 33, No. 8. 1619–1632.
    Z. Kalal, K. Mikolajczyk, and J. Matas. 2010. Forward-Backward Error: Automatic Detection of Tracking Failures. In In Proc. of the International Conference on Pattern Recognition (ICPR2010).
    Z. Kalal, K. Mikolajczyk, and J. Matas. 2012. Tracking-learning-detection. In IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.37, No.7. 1409–1422. A. Lukezi˘ c, T. Vojir, L. C. Zajc, J. Matas, and M. Kristan. 2018. Discriminative Correlation ˘ Filter with Channel and Spatial Reliability. In Proc of the International Conference on Computer Vision (CVPR),. 6309–6318.

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