Call for Paper - January 2022 Edition
IJCA solicits original research papers for the January 2022 Edition. Last date of manuscript submission is December 20, 2021. Read More

Simultaneous Localization and Mapping for Trajectory Prediction of Tennis Ball

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Zeeshan Ali Haq
10.5120/ijca2016912186

Zeeshan Ali Haq. Simultaneous Localization and Mapping for Trajectory Prediction of Tennis Ball. International Journal of Computer Applications 153(12):13-17, November 2016. BibTeX

@article{10.5120/ijca2016912186,
	author = {Zeeshan Ali Haq},
	title = {Simultaneous Localization and Mapping for Trajectory Prediction of Tennis Ball},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2016},
	volume = {153},
	number = {12},
	month = {Nov},
	year = {2016},
	issn = {0975-8887},
	pages = {13-17},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume153/number12/26541-2016912186},
	doi = {10.5120/ijca2016912186},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Object detection and tracking its movement is an important aspect in today’s sports broadcasting industry. It helps in post-match analysis, studying previous matches to find drawbacks in one’s technique and also helps in discussion and debate. Detecting a moving object is a cumbersome technique as it involves a very complex human movement as well. In this paper, movement of tennis ball is detected, tracked and its a-priori path has been studied to predict the posteriori movement. The detection of the tennis ball is performed using Matlab. Tracking and path prediction is performed using Kalman filter. This filter works on an algorithm having two stages: prediction and updating. The ball detection accuracy of 96% has been achieved. The parameters of a moving ball that has been studied are its acceleration, process noise and measurement noise. The error found in tracking the ball while varying its various parameters is also discussed.

References

  1. Needham, Chris J., and Roger D. Boyle. "Tracking multiple sports players through occlusion, congestion and scale." BMVC. Vol. 1. No. 1. 2001.
  2. Farin, D. "Current and emerging topics in sports video processing." 2005 IEEE International Conference on Multimedia and Expo. IEEE, 2005.
  3. Yu, Xinguo, et al. "Trajectory-based ball detection and tracking in broadcast soccer video." Multimedia, IEEE Transactions on 8.6 (2006): 1164-1178.
  4. Sýkora, D., Sedlacek, D., & Riege, K. (2008, May). Real-time Color Ball Tracking for Augmented Reality. In IPT/EGVE (pp. 9-16).
  5. Larson, Noble G., and Kent A. Stevens. "Automated camera-based tracking system for sports contests." U.S. Patent No. 5,363,297. 8 Nov. 1994.
  6. Kim, S. W., Yun, K., Yi, K. M., Kim, S. J., & Choi, J. Y. (2013). Detection of moving objects with a moving camera using non-panoramic background model. Machine vision and applications, 24(5), 1015-1028.
  7. Teachabarikiti, K., Chalidabhongse, T. H., & Thammano, A. (2010, December). Players tracking and ball detection for an automatic tennis video annotation. In Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on (pp. 2461-2494). IEEE.
  8. Prabhakar, N., et al. "Object tracking using frame differencing and template matching." Research Journal of Applied Sciences, Engineering and Technology 4.24 (2012): 5497-5501.
  9. Claus D., FitzgibbonA.: Reliable fiducial detection in natural scenes. In Proceedings of European Conference on Computer Vision (2004), pp. 469–480.
  10. Yan, F. (2007). Tennis Ball Tracking for Automatic Annotation of Broadcast Tennis Video (Doctoral dissertation, University of Surrey).
  11. G. S. Pingali, Y. Jean, and I. Carlbom. Real time tracking for enhanced tennis broadcasts. In IEEE International Conference on Computer Vision and Pattern Recognition, pages 260–265, 1998.
  12. R. Streit and T. Luginbuhl. Probabilistic multi-hypothesis tracking. Technical Report, 1995.
  13. H. Miyamori and S. Iisaku. Video annotation for content-based retrieval using human behavior analysis and domain knowledge. In International Conference on Automatic Face and Gesture Recognition, pages 320–325, 2000.
  14. X. Yu, C. Sim, J. R. Wang, and L. Cheong. A trajectory-based ball detection and tracking algorithm in broadcast tennis video. In International Conference on Image Processing, volume 2, pages 1049–1052, 2004.
  15. Mehta, Rabindra D. "Aerodynamics of sports balls." Annual Review of Fluid Mechanics 17.1 (1985): 151-189.
  16. Yu, X., Sim, C. H., Wang, J. R., & Cheong, L. F. (2004, October). A trajectory-based ball detection and tracking algorithm in broadcast tennis video. In Image Processing, 2004. ICIP'04. 2004 International Conference on (Vol. 2, pp. 1049-1052). IEEE.

Keywords

Trajectory prediction, Kalman filter, objects tracking, moving object detection.