Tracking People in Broadcast Sports

  • Angela Yao
  • Dominique Uebersax
  • Juergen Gall
  • Luc Van Gool
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6376)


We present a method for tracking people in monocular broadcast sports videos by coupling a particle filter with a vote-based confidence map of athletes, appearance features and optical flow for motion estimation. The confidence map provides a continuous estimate of possible target locations in each frame and outperforms tracking with discrete target detections. We demonstrate the tracker on sports videos, tracking fast and articulated movements of athletes such as divers and gymnasts and on non-sports videos, tracking pedestrians in a PETS2009 sequence.


Local Binary Pattern Soccer Player Camera Motion Motion Blur Sport Video 
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.
    Kang, J., Cohen, I., Medioni, G.: Soccer player tracking across uncalibrated camera streams. In: IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, VS-PETS (2003)Google Scholar
  2. 2.
    Choi, K., Seo, Y., Lee, S.: Probabilistic tracking of soccer players and ball. In: ACCV (2004)Google Scholar
  3. 3.
    Okuma, K., Taleghani, A., de Freitas, N., Little, J., Lowe, D.: A boosted particle filter: Multitarget detection and tracking. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 28–39. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Kristan, M., Pers, J., Perse, M., Kovacic, S.: Closed-world tracking of multiple interacting targets for indoor-sports applications. CVIU 113(5), 598–611 (2009)Google Scholar
  5. 5.
    Breitenstein, M., Reichlin, F., Leibe, B., Koller-Meier, E., Gool, L.V.: Robust tracking-by-detection using a detector confidence particle filter. In: ICCV (2009)Google Scholar
  6. 6.
    Hess, R., Fern, A.: Discriminatively trained particle filters for complex multi-object tracking. In: CVPR (2009)Google Scholar
  7. 7.
    Doucet, A., Freitas, N.D., Gordon, N. (eds.): Sequential Monte Carlo Methods in Practice. Springer, New York (2001)zbMATHGoogle Scholar
  8. 8.
    Gall, J., Lempitsky, V.: Class-specific hough forests for object detection. In: CVPR (2009)Google Scholar
  9. 9.
    Rodriguez, M.D., Ahmed, J., Shah, M.: Action mach a spatio-temporal maximum average correlation height filter for action recognition. In: CVPR (2008)Google Scholar
  10. 10.
    Ferryman, J., Shahrokni, A.: Pets2009: Dataset and challenge. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (2009)Google Scholar
  11. 11.
    Sullivan, J., Carlsson, S.: Tracking and labelling of interacting multiple targets. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 232–244. Springer, Heidelberg (2006)Google Scholar
  12. 12.
    Lu, W.L., Little, J.J.: Tracking and recognizing actions at a distance. In: Proceedings of the ECCV Workshop on Computer Vision Based Analysis in Sport Environments (CVBASE 2006), Graz, Austria (May 2006)Google Scholar
  13. 13.
    Liu, G., Tang, X., Cheng, H.D., Huang, J., Liu, J.: A novel approach for tracking high speed skaters in sports using a panning camera. Pattern Recogn. 42(11), 2922–2935 (2009)CrossRefGoogle Scholar
  14. 14.
    Khatoonabadi, S.H., Rahmati, M.: Automatic soccer players tracking in goal scenes by camera motion elimination. Image Vision Comput. 27(4), 469–479 (2009)CrossRefGoogle Scholar
  15. 15.
    Collins, R., Liu, Y., Leordeanu, M.: On-line selection of discriminative tracking features. TPAMI 27(1), 1631 (2005)Google Scholar
  16. 16.
    Grabner, H., Leistner, C., Bischof, H.: Semi-supervised on-line boosting for robust tracking. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 234–247. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  17. 17.
    Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR (2009)Google Scholar
  18. 18.
    Andriluka, M., Roth, S., Schiele, B.: People-tracking-by-detection and people-detection-by-tracking. In: CVPR (2008)Google Scholar
  19. 19.
    Leibe, B., Leonardis, A., Schiele, B.: Robust object detection with interleaved categorization and segmentation. IJCV 77(1-3), 259–289 (2008)CrossRefGoogle Scholar
  20. 20.
    Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: CVPR (2006)Google Scholar
  21. 21.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR (2005)Google Scholar
  22. 22.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. TPAMI 24(7), 971–987 (2002)Google Scholar
  23. 23.
    Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)Google Scholar
  24. 24.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge, VOC 2007 Results (2007),

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Angela Yao
    • 1
  • Dominique Uebersax
    • 1
  • Juergen Gall
    • 1
  • Luc Van Gool
    • 1
    • 2
  1. 1.ETH ZurichSwitzerland
  2. 2.KU LeuvenBelgium

Personalised recommendations