“Sword Tracer: Visualization of Sword Trajectories in Fencing”

  • ©Masaki Takahashi, Shinsuke Yokozawa, Hideki Mitsumine, Tetsuya Itsuki, Masato Naoe, and Satoshi Funaki

  • ©Masaki Takahashi, Shinsuke Yokozawa, Hideki Mitsumine, Tetsuya Itsuki, Masato Naoe, and Satoshi Funaki

  • ©Masaki Takahashi, Shinsuke Yokozawa, Hideki Mitsumine, Tetsuya Itsuki, Masato Naoe, and Satoshi Funaki

  • ©Masaki Takahashi, Shinsuke Yokozawa, Hideki Mitsumine, Tetsuya Itsuki, Masato Naoe, and Satoshi Funaki

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Entry Number: 25

Title:

    Sword Tracer: Visualization of Sword Trajectories in Fencing

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Abstract:


    This paper describes a system for visualizing sword trajectories in fencing. Fencing swords are very thin and move so fast that it is difficult for audiences to follow their movements even in slow-motion video replays. The system thus tracks the tips of the swords in the image coordinates and visualizes their movements with computer graphics (CG). We call it “Sword Tracer.”

References:


    G. Anusha and E. G. Jule. 2014. Improving the performance of video tracking using SVM. In Proceedings of the International Journal of Engineering Trends and Technology (IJETT), Vol.11, No.3. 133–139.
    M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. 2002. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. (2002), 174–188.
    M. H. Babenko, B. Yang and S. Belongie. 2011. Robust object tracking with online multiple instance learning. (2011), 1619–1632.
    H. Grabner, M. Grabner, and H. Bischof. 2006. Real-time tracking via on-line boosting. In In Proceedings of British Machine Vision Conference (BMVC), Vol. 1. 47–56.
    G. B. Guerra-filho. 2005. Optical motion capture: Theory and implementation. (2005), 61–89.
    M. Tang and J. Feng. 2015. Multi-kernel correlation filter for visual tracking. In In Proceedings of the International Conference on Computer Vision (ICCV). 3038–3046.

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