Estimation of 3D Instantaneous Motion of a Ball from a Single Motion-Blurred Image

  • Giacomo Boracchi
  • Vincenzo Caglioti
  • Alessandro Giusti
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 24)


We present a single-image algorithm for reconstructing the 3D velocity, the 3D spin axis, and the angular speed of a moving ball. Peculiarity of the proposed algorithm is that this reconstruction is achieved by accurately analyzing the blur produced by the ball motion during the exposure. We combine image analysis techniques in order to obtain 3D estimates, that are then integrated into a geometrical model for recovering the 3D motion.

The algorithm is validated with experiments on both synthetic and camera images. In a broader scenario, we exploit this specic problem for discussing motivations, advantages, and limitations of reconstructing 3D motion from motion blur.


Point Spread Function Angular Speed Motion Blur Ball Motion Orientation Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Boracchi, G., Caglioti, V., Giusti, A.: Ball position and motion reconstruction from blur in a single perspective image. In: Proceedings of ICIAP 2007, Modena (2007)Google Scholar
  2. 2.
    Boracchi, G., Caglioti, V., Giusti, A.: Single-image 3d reconstruction of ball velocity and spin from motion blur. In: VISAPP 2008, the 3rd International Conference on Computer Vision Theory and Applications, Funchal, Madeira - Portugal, January 22-25 (2008)Google Scholar
  3. 3.
    Fergus, R., Singh, B., Hertzmann, A., Roweis, S.T., Freeman, W.T.: Removing camera shake from a single photograph. In: ACM SIGGRAPH 2006 Papers (2006)Google Scholar
  4. 4.
    Levin, A.: Blind motion deblurring using image statistics. In: Schölkopf, B., Platt, J., Hoffman, T. (eds.) Advances in Neural Information Processing Systems 19. MIT Press, Cambridge (2007)Google Scholar
  5. 5.
    Jia, J.: Single image motion deblurring using transparency. In: Proceedings of CVPR 2007, Minneapolis (2007)Google Scholar
  6. 6.
    Klein, G., Drummond, T.: A single-frame visual gyroscope. In: Proc. British Machine Vision Conference (BMVC 2005), Oxford, BMVA, vol. 2, pp. 529–538 (2005)Google Scholar
  7. 7.
    Levin, A., Fergus, R., Durand, F., Freeman, W.T.: Image and depth from a conventional camera with a coded aperture. ACM Trans. Graph. 26, 70 (2007)CrossRefGoogle Scholar
  8. 8.
    Rekleitis, I.M.: Steerable filters and cepstral analysis for optical flow calculation from a single blurred image. In: Vision Interface, Toronto, pp. 159–166 (1996)Google Scholar
  9. 9.
    Lin, H.Y., Chang, C.H.: Automatic speed measurements of spherical objects using an off-the-shelf digital camera. In: IEEE International Conference on Mechatronics, ICM 2005, pp. 66–71 (2005)Google Scholar
  10. 10.
    Gopal Pingali, A.O., Jean, Y.: Ball tracking and virtual replays for innovative tennis broadcasts. In: Proc. of ICPR 2000 Conference, Washington, p. 4152. IEEE Computer Society, Los Alamitos (2000)Google Scholar
  11. 11.
    Ren, J., Orwell, J., Xu, G.J.: A general framework for 3d soccer ball estimation and tracking. In: Proc. of ICIP 2004 Conference (2004)Google Scholar
  12. 12.
    Rubin, J., Burkhard, C., Wuensche, L.C., Stevens, C.: Computer vision for low cost 3-d golf ball and club tracking. In: Proc. of Image and Vision Computing, New Zealand (2005)Google Scholar
  13. 13.
    Reid, I.D., North, A.: 3d trajectories from a single viewpoint using shadows. In: Proc. of BMVC 1998 Conference (1998)Google Scholar
  14. 14.
    Kim, T., Seo, Y., Hong, K.S.: Physics-based 3d position analysis of a soccer ball from monocular image sequences. In: Proc of ICCV 1998 Conference, pp. 721–726 (1998)Google Scholar
  15. 15.
    Ohno, Y., Miura, J., Shirai, Y.: Tracking players and estimation of the 3d position of a ball in soccer games. In: ICPR, pp. 1145–1148 (2000)Google Scholar
  16. 16.
    Caglioti, V., Giusti, A.: Recovering ball motion from a single motion-blurred image. Computer Vision and Image Understanding (in press)Google Scholar
  17. 17.
    Bertero, M., Boccacci, P.: Introduction to Inverse Problems in Imaging. Institute of Physics Publishing (1998)Google Scholar
  18. 18.
    Smith, A.R., Blinn, J.F.: Blue screen matting. In: SIGGRAPH 1996: Proc. of the 23rd annual conference on Computer graphics and interactive techniques, pp. 259–268 (1996)Google Scholar
  19. 19.
    Mishima, Y.: Soft edge chroma-key generation based upon hexoctahedral color space, U.S. Patent 5,355,174 (1993)Google Scholar
  20. 20.
    Caglioti, V., Giusti, A.: On the apparent transparency of a motion blurred object. In: Proc. of ICCV workshop on Photometric Analysis in Computer Vision, PACV 2007 (2007)Google Scholar
  21. 21.
    Giusti, A., Caglioti, V.: Isolating motion and color in a motion blurred image. In: Proc. of BMVC 2007 (2007)Google Scholar
  22. 22.
    Yitzhaky, Y., Kopeika, N.S.: Identification of blur parameters from motion-blurred images. In: Proc. SPIE, vol. 2847, pp. 270–280 (1996)Google Scholar
  23. 23.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, pp. 147–151 (1988)Google Scholar
  24. 24.
    Blender 3d modeler,
  25. 25.
    Donoho, D.L., Johnstone, I.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81, 425–455 (1994)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Giacomo Boracchi
    • 1
  • Vincenzo Caglioti
    • 1
  • Alessandro Giusti
    • 1
  1. 1.Dipartimento di Elettronica e InformazionePolitecnico di MilanoMilano

Personalised recommendations