International Journal of Computer Vision

, Volume 75, Issue 1, pp 173–187 | Cite as

Live 3D Video in Soccer Stadium

  • Yuichi Ohta
  • Itaru Kitahara
  • Yoshinari Kameda
  • Hiroyuki Ishikawa
  • Takayoshi Koyama
Article

Abstract

This paper proposes a method to realize a 3D video system that can capture video data from multiple cameras, reconstruct 3D models, transmit 3D video streams via the network, and display them on remote PCs. All processes are done in real time. We represent a player with a simplified 3D model consisting of a single plane and a live video texture extracted from multiple cameras. This 3D model is simple enough to be transmitted via a network. A prototype system has been developed and tested at actual soccer stadiums. A 3D video of a typical soccer scene, which includes more than a dozen players, was processed at video rate and transmitted to remote PCs through the internet at 15–24 frames per second.

Keywords

virtualized reality 3D video image-based rendering 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Akenine-Moller, T. and Haines, E. 2002. Real-Time Rendering, AK Peters Ltd., ISBN 1568811829.Google Scholar
  2. Antonini, G., Martinez, S.V., Bierlaire, M., and Thiran, J.P. 2006. Behavioral priors for detection and tracking of pedestrians in video sequences. International Journal of Computer Vision, 69(2):159–180.CrossRefGoogle Scholar
  3. Barnard, M. and Odobez, J.M. 2004. Robust playfield segmentation using MAP adaptation. In International Conference on Pattern Recognition, vol. 3, pp. 610–613.Google Scholar
  4. Cheung, G.K.M., Kanade, T., Bouguet, J.Y., and Holler, M. 2000. A real time system for robust 3D voxel reconstruction of human motions. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR2000), pp. 714-729.Google Scholar
  5. Cheng, K.M., Baker, S., and Kanade, T. 2005a. Shape-from-silhouette across time part I: Theory and algorithms. International Journal of Computer Vision, 62(3):221–247.CrossRefGoogle Scholar
  6. Cheng, K.M., Baker, S., and Kanade, T. 2005b. Shape-from-silhouette across time part II: Applications to human modeling and markerless motion tracking. International Journal of Computer Vision, 63(3):225–245.CrossRefGoogle Scholar
  7. Collins, R.T. 1996. A space-sweep approach to true multi-image matching. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’96), pp. 358–363.Google Scholar
  8. Deutscher, J. and Reid, I. 2005. Articulated body motion capture by stochastic search. International Journal of Computer Vision, 61(2):185–205.CrossRefGoogle Scholar
  9. Efros, A.A., Berg, A.C., Mori, G., and Malik, J. 2003. Recognizing action at a distance. IEEE International Conference on Computer Vision, 2:726–733.Google Scholar
  10. Figueroa, P., Leite, N., Barros, R.M.L, Cohen, I., and Medioni, G. 2004. Tracking soccer players using the graph representation. International Conference on Pattern Recognition, 4:787–790.Google Scholar
  11. Iwase, S. and Saito, H. 2004. Parallel tracking of all soccer players by integrating detected positions in multiple view images. International Conference on Pattern Recognition, 4:751–754.Google Scholar
  12. Kanade, T., Rander, P.W., and Narayanan, P.J. 1997. Virtualized reality: constructing virtual worlds from real scenes. IEEE Multimedia, 4(1):34–47.CrossRefGoogle Scholar
  13. Kim, T., Seo, Y., and Hong, K. 1998. Physics-based 3D position analysis of a soccer ball from monocular image sequences. In IEEE International Conference on Computer Vision, pp. 721–726.Google Scholar
  14. Kitahara, I., Saito, H., Akimichi, S., Ono, T., Ohta Y., and Kanade, T. 2001. Large-scale virtualized reality. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2001), Technical Sketches.Google Scholar
  15. Matusik, W., Buehler, C., Raskar, R., Gortler S.J., and McMillan, L. 2000. Image-based visual hulls. In ACM SIGGRAPH 2000, pp. 369–374.Google Scholar
  16. Misu, T., Naemura, M., Zheng, W., Izumi, Y., and Fukui, K. 2002. Robust tracking of soccer players based on data fusion. In International Conference on Pattern Recognition, 1:556–561.Google Scholar
  17. Moezzi, S., Katkere, A., Kuramura, D.Y., and Jain, R. 1996. Interactive three-dimensional digital video. In Proc. of IEEE International Conference on Multimedia Computing and Systems (ICMCS’96), pp. 358–361.Google Scholar
  18. Narayanan, P.J., Rander, P., and Kanade, T. 1998. Constructing virtual worlds using dense stereo. In Proc. of the International Conference on Computer Vision (ICCV’98), pp. 3–10.Google Scholar
  19. Ohno, Y., Miura, J., and Shirai, Y. 2000. Tracking players and estimation of the 3D position of a ball in soccer games. In International Conference on Pattern Recognition, vol 2, pp. 145–148.Google Scholar
  20. Pan, Z. and Ngo, C.W. 2004. Novel seed selection for multiple objects detection and tracking. In International Conference on Pattern Recognition, vol 2, pp. 744–747.Google Scholar
  21. Potmesil, M. 1987. Generating octree model of 3D objects from their silhouette in a sequence of images. Computer Vision, Graphics and Image Processing (CVGIP), 40:1–29.CrossRefGoogle Scholar
  22. Ramanan, D., Forsyth, A., and Zisserman, A. 2005. Strike a pose: tracking people by finding stylized poses. In IEEE Conference on Computer Vision and Pattern Recognition, vol 1, pp. 271–278.Google Scholar
  23. Rosales, R. and Sclaroff, S. 2006. Combining generative and discriminative models in a framework for articulated pose estimation. International Journal of Computer Vision, 67(3):251–276.CrossRefGoogle Scholar
  24. Saito, H. and Kanade, T. 1999. Shapae reconstruction in projective grid space from large number of images. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’99), pp. 49– 54.Google Scholar
  25. Seitz, S.M. and Dyer, C.R. 1998. Photorealistic scene reconstruction by voxel coloring. In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’98), pp. 1067–1073.Google Scholar
  26. Stauffer, C. and Grimson, W. 1999. Adaptive background mixture for real-time tracking. IEEE Conference on Computer Vision and Pattern Recognition, pp. 246–252.Google Scholar
  27. Sugawara, S., Suzuki, G., Nagashima, Y., Matsuura, M., Tanigawa, H., and Morimichi, M. 1994. Interspace: Networked virtual world for virtual communications. IEICE Transaction on Information and Systems, E77-D(2):1344–1349.Google Scholar
  28. Sullivan, S. and Ponce, J. 1998. Automatic model construction, pose estimation and objects recognition from photographs using triangular splines. In Proc. of the International Conference on Computer Vision (ICCV’98), pp. 510–516.Google Scholar
  29. Szelisli, R. 1993. Rapid octree construction from image sequences. Computer Vision, Graphics and Image Processing (CVGIP), 58:23–32.CrossRefGoogle Scholar
  30. Takeuchi, T. and Valois, K.D. 2002. Motion sharpening in moving natural images. Journal of Vision, 2(7):377.CrossRefGoogle Scholar
  31. Veit, T., Cao, F., and Bouthemy, P. 2006. An a contrario decision framework for region-based motion detection. International Journal of Computer Vision, 68(2):163–178.CrossRefGoogle Scholar
  32. Veloso, M., Stone, P., Han, K., and Achim, S. 1998. The CMUnited-97 small-robot team. Robot Soccer World Cup I (RoboCup-97), Lecture Notes in Artificial Intelligence, pp. 242– 256.Google Scholar
  33. Viola, P., Jones M.J., and Snow, D. 2005. Detecting pedestrians using patterns of motion and appearance. International Journal of Computer Vision, 63(2):153–161.CrossRefGoogle Scholar
  34. Wada, T., Wu, X., and Matsuyama, T. 2000. Homography based parallel volume intersection: Toward real-time volume reconstruction using active cameras. In Proc. of Computer Architectures for Machine Perception 2000, pp. 331–339.Google Scholar
  35. Würmlin, S., Lamboray, E.O., Staadt, G., and Gross, M.H. 2002. 3D Video Recorder. Proceedings of Pacific Graphics ’02, IEEE Computer Society Press, pp. 325–334.Google Scholar
  36. Yamada, A., Shirai, Y., and Miura, J. 2002. Tracking players and a ball in video image sequence and estimating camera parameters for 3D interpretation of soccer games. International Conference on Pattern Recognition, 1:303–306.Google Scholar
  37. Yang, R., Kurashima, C., Nashel, A., Towles, H., Lastra, A., and Fuchs, H. 2002. Creating adaptive views for group video teleconferencing -an image-based approach. International Workshop on Immersive Telepresence (ITP 2002).Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Yuichi Ohta
    • 1
  • Itaru Kitahara
    • 1
  • Yoshinari Kameda
    • 1
  • Hiroyuki Ishikawa
    • 1
  • Takayoshi Koyama
    • 1
  1. 1.University of TsukubaTsukubaJapan

Personalised recommendations