Karate Video Reconstruction to Observe Player Motion and Flow of Competition

Kazumoto Tanaka

Abstract


It is well known that sport skill learning for trainees is facilitated via video observation of both the players’ actions and the overall flow of competition on the playing field. A video zoomed in on a player’s actions is suitable for learning the technical skills of the player, and a video showing all the players on the competition court is suitable for learning tactical skills such as player formation. The purpose of this study is to establish a method that can make narrow field-of-view videos available for observing the player positioning throughout the competition court. This paper focuses on karate competition videos and proposes an image processing method that extracts the image of the player area from the video frame and superimposes it on a standard karate court model image. Before the superimposition, the court model image is homographically transformed so that it fits the court in the video image. Using this approach, a player-focused video is reconstructed as a video of the entire competition field. For the reconstruction, a vertex matching method is developed for homography matrix calculation, and another calculation method for homography matrix calculation using only two straight lines is also developed for images that lack vertex information. Finally, an experimental result shows that a karate player-focused video is transformed successfully into a video of the entire karate competition field. Future work will focus on experimentally verifying the effectiveness of the method to the skill learning.

Keywords


video reconstruction; video observation; homographic transformation; camera calibration

Full Text:

PDF

References


F. Potdevin, O. Vors, A. Huchez, M. Lamour, K. Davids, and C. Schnitzler, “How can video feedback be used in physical education to support novice learning in gymnastics? Effects on motor learning, self-assessment and motivation,” Physical Education and Sport Pedagogy, vol. 23, no. 6, pp. 559-574, Jun. 2018.

S. B. Nicholls, N. James, E. Bryant, and J. Wells, “The implementation of performance analysis and feedback within Olympic sport: The performance analyst's perspective,”. International Journal of Sports Science & Coaching, vol. 14, no.1, pp. 63-71, Oct. 2018.

D. Farin, J. Han, and P. H. N. de With, “Fast Camera Calibration for the Analysis of Sport Sequences,” in Proc. IEEE International Conference on Multimedia and Expo, Amsterdam, Netherlands, 2005, DOI: 10.1109/ICME.2005.1521465.

J. Han, D. Farin and P. H. N. de With, “Broadcast Court-Net Sports Video Analysis Using Fast 3-D Camera Modeling, ” IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, no. 11, pp. 1628-1638, Nov. 2008, DOI: 10.1109/TCSVT.2008.2005611.

Q. Yao, A. Kubota, K. Kawakita, K. Nonaka, H. Sankoh, and S. Naito, “Fast Camera Self-Calibration for Synthesizing Free Viewpoint Soccer Video,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, New Orleans, LA, 2017, pp. 1612-1616, DOI: 10.1109/ICASSP.2017.7952429.

S. Liu, J. Chen, C. Chang and Y. Ai, “A New Accurate and Fast Homography Computation Algorithm for Sports and Traffic Video Analysis, “ IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 10, pp. 2993-3006, Oct. 2018, DOI: 10.1109/TCSVT.2017.2731781.

W. Jiang, J. C. Gamboa Higuera, B. Angles, W. Sun, M. Javan, and K. M. Yi, “Optimizing Through Learned Errors for Accurate Sports Field Registration,” in Proc. IEEE Winter Conference on Applications of Computer Vision, Snowmass, CO, 2020, pp. 201-210, DOI: 10.1109/WACV45572.2020.9093581.

H. Tsurusaki, K. Nonaka, R. Watanabe, T. Konno, and S. Naito, “Sports Camera Calibration Using Flexible Intersection Selection and Refinement,” ITE Transactions on Media Technology and Applications, vol. 9, no. 1, pp.95-104, Jan. 2021, DOI: https://doi.org/10.3169/mta.9.95.

G. Thomas, “Real-time Camera Tracking Using Sports Pitch Markings,” Journal of Real-Time Image Processing, vol. 2, pp. 117-132, 2007, DOI: 10.1007/s11554-007-0041-1.

J. Chen, F. Zhu, and J. J. Little, “A Two-Point Method for PTZ Camera Calibration in Sports,” in Proc. IEEE Winter Conference on Applications of Computer Vision, Lake Tahoe, NV, 2018, pp. 287-295, DOI: 10.1109/WACV.2018.00038.

R. A. Sharma, B. Bhat, V. Gandhi, and C. V. Jawahar, “Automated Top View Registration of Broadcast Football Videos,” in Proc. IEEE Winter Conference on Applications of Computer Vision, Lake Tahoe, NV, 2018, pp. 305-313, DOI: 10.1109/WACV.2018.00040.

J. Chen, and J. J. Little, “Sports Camera Calibration via Synthetic Data,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, 2019.

W.-L. Lu, J.-A. Ting, J. J. Little, and K. P. Murphy, “Learning to Track and Identify Players from Broadcast Sports Videos,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35. no. 7, pp. 1704-1716, Jul. 2013, DOI: 10.1109/TPAMI.2012.242.

P.-C. Wen, W.-C. Cheng, Y.-S. Wang, H.-K. Chu, N. C. Tang, and H.-Y. M. Liao, “Court Reconstruction for Camera Calibration in Broadcast Basketball Videos,” IEEE Transactions on Visualization and Computer Graphics, vol. 22, no. 5, pp. 1517-1526, May 2016, DOI: 10.1109/TVCG.2015.2440236.

R. Zeng, R. Lakemond, S. Denman, S. Sridharan, C. Fookes, and S. Morgan, “Calibrating Cameras in Poor-conditioned Pitch-based Sports Games,” in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, Calgary, AB, 2018, pp. 1902-1906, DOI: 10.1109/ICASSP.2018.8461667.

N. Homayounfar, S. Fidler and R. Urtasun, “Sports Field Localization via Deep Structured Models,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, 2017, pp. 4012-4020, DOI: 10.1109/CVPR.2017.427.

L. Citraro, P. Márquez-Neila, S. Savarè, V. Jayaram, C. Dubout, F. Renaut, A. Hasfura, H. B. Shitrit, P. Fua, “Real-time Camera Pose Estimation for Sports Fields,” Machine Vision and Applications, vol. 31, no. 16, Mar. 2020, DOI: https://doi.org/10.1007/s00138-020-01064-7.

X. Nie, S. Chen, and R. Hamid, “A Robust and Efficient Framework for Sports-Field Registration,” in Proc. IEEE/CVF Winter Conference on Applications of Computer Vision, Virtual, 2021, pp. 1936-1944.

K. He, G. Gkioxari, P. Dollar, and R. Girshick, “Mask R-CNN,” in Proc. IEEE International Conference on Computer Vision, Venice, Italy, 2018, pp. 2961-2969.

Y. Takezawa, M. Hasegawa, and S. Tabbone, “Robust Perspective Rectification of Camera-Captured Document Images,” in Proc. IAPR International Conference on Document Analysis and Recognition, Kyoto, Japan, 2017, pp. 27-32, DOI: 10.1109/ICDAR.2017.345.




DOI: http://dx.doi.org/10.18517/ijaseit.12.4.14660

Refbacks

  • There are currently no refbacks.


Published by INSIGHT - Indonesian Society for Knowledge and Human Development