A vision-based system to support tactical and physical analyses in futsal

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.


This paper presents a vision-based system to support tactical and physical analyses of futsal teams. Most part of the current analyses in this sport are manually performed, while the existing solutions based on automatic approaches are frequently composed of costly and complex tools, developed for other kind of team sports, making it difficult their adoption by futsal teams. Our system, on the other hand, represents a simple yet efficient dedicated solution, which is based on the analyses of image sequences captured by a single stationary camera used to obtain top-view images of the entire court. We use adaptive background subtraction and blob analysis to detect players, as well as particle filters to track them in every video frame. The system determines the distance traveled by each player, his/her mean and maximum speeds, as well as generates heat maps that describe players’ occupancy during the match. To present the collected data, our system uses a specially developed mobile application. Experimental results with image sequences of an official match and a training match show that our system provides data with global mean tracking errors below 40 cm, demanding on 25 ms to process each frame and, thus, demonstrating its high application potential.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9


  1. 1.

    Kristan, M., Perš, J., Perše, M., Kovačič, S.: Closed-world tracking of multiple interacting targets for indoor-sports applications. Comput. Vis. Image Underst. 113(5), 598–611 (2009)

    Article  Google Scholar 

  2. 2.

    Morais, E., Ferreira, A., Cunha, S.A., Barros, R.M., Rocha, A., Goldenstein, S.: A multiple camera methodology for automatic localization and tracking of futsal players. Pattern Recogn. Lett. 39, 21–30 (2014)

    Article  Google Scholar 

  3. 3.

    Perl, J., Grunz, A., Memmert, D.: Tactics analysis in soccer—an advanced approach. Int. J. Comput. Sci. Sport 12(1), 33–44 (2013)

    Google Scholar 

  4. 4.

    Wang, X., Ablavsky, V., Ben Shitrit, H., Fua, P.: Take your eyes off the ball: improving ball-tracking by focusing on team play. Comput. Vis. Image Underst. 119, 102–115 (2014)

    Article  Google Scholar 

  5. 5.

    Niu, Z., Gao, X., Tian, Q.: Tactic analysis based on real-world ball trajectory in soccer video. Pattern Recogn. 45(5), 1937–1947 (2012)

    Article  Google Scholar 

  6. 6.

    Zhu, G., Huang, Q., Xu, C., Rui, Y., Jiang, S., Gao, W., Yao, H.: Trajectory based event tactics analysis in broadcast sports video. In: International Conference on Multimedia, pp. 58–67 (2007)

  7. 7.

    D’Orazio, T., Leo, M.: A review of vision-based systems for soccer video analysis. Pattern Recogn. 43(8), 2911–2926 (2010)

    Article  Google Scholar 

  8. 8.

    Catapult. http://www.catapultsports.com. Accessed 30 June 2015

  9. 9.

    Inmotio. http://www.inmotio.eu. Accessed 30 June 2015

  10. 10.

    Opta. http://www.optasports.com/. Accessed 1 July 2015

  11. 11.

    SportVU. http://www.stats.com. Accessed 30 June 2015

  12. 12.

    Pádua, P.H.C., Pádua, F.L.C., Sousa, M.T.D., Pereira, M.A.: Particle filter-based predictive tracking of futsal players from a single stationary camera. In: Conference on Graphics, Patterns and Images, pp. 134–141 (2015)

  13. 13.

    Zivkovic, Z.: Improved adaptive gaussian mixture model for background subtraction. In: International Conference on Pattern Recognition, pp. 28–31 (2004)

  14. 14.

    Kuhn, H.W.: The hungarian method for the assignment problem. Naval Res. Logist. Q. 2(1–2), 83–97 (1955)

    MathSciNet  Article  MATH  Google Scholar 

  15. 15.

    Santiago, C.B., Sousa, A., Estriga, M.L., Reis, L.P., Lames, M.: Survey on team tracking techniques applied to sports. In: International Conference on Autonomous and Intelligent Systems, pp. 1–6 (2010)

  16. 16.

    Mandeljc, R., Kovačič, S., Kristan, M., Perš, J., et al.: Tracking by identification using computer vision and radio. Sensors 13(1), 241–273 (2012)

    Article  Google Scholar 

  17. 17.

    Beetz, M., Kirchlechner, B., Lames, M.: Computerized real-time analysis of football games. IEEE Pervasive Comput. 4(3), 33–39 (2005)

    Article  Google Scholar 

  18. 18.

    Zebra. https://www.zebra.com/. Accessed 1 July 2015

  19. 19.

    Wisbey, B., Montgomery, P.G., Pyne, D.B., Rattray, B.: Quantifying movement demands of AFL football using GPS tracking. J. Sci. Med. Sport 13(5), 531–536 (2010)

    Article  Google Scholar 

  20. 20.

    Ben Shitrit, H., Berclaz, J., Fleuret, F., Fua, P.: Multi-commodity network flow for tracking multiple people. IEEE Trans. Pattern Anal. Mach. Intell. 36(8), 1614–1627 (2014)

    Article  MATH  Google Scholar 

  21. 21.

    Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. IEEE Trans. Pattern Anal. Mach. Intell. 33(9), 1806–1819 (2011)

    Article  Google Scholar 

  22. 22.

    Figueroa, P.J., Leite, N.J., Barros, R.M.: Background recovering in outdoor image sequences: an example of soccer players segmentation. Image Vis. Comput. 24(4), 363–374 (2006)

    Article  Google Scholar 

  23. 23.

    Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multicamera people tracking with a probabilistic occupancy map. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 267–282 (2008)

    Article  Google Scholar 

  24. 24.

    Joo, S.W., Chellappa, R.: A multiple-hypothesis approach for multiobject visual tracking. IEEE Trans. Image Process. 16(11), 2849–2854 (2007)

    MathSciNet  Article  Google Scholar 

  25. 25.

    Nillius, P., Sullivan, J., Carlsson, S.: Multi-target tracking-linking identities using bayesian network inference. In: Conference on Computer Vision and Pattern Recognition, pp. 2187–2194 (2006)

  26. 26.

    Renno, J.P., Orwell, J., Thirde, D., Jones, G.A.: Shadow classification and evaluation for soccer player detection. In: British Machine Vision Conference, pp. 1–10 (2004)

  27. 27.

    Xu, M., Orwell, J., Lowey, L., Thirde, D.: Architecture and algorithms for tracking football players with multiple cameras. Vis. Image Signal Process. 152(2), 232–241 (2005)

    Article  Google Scholar 

  28. 28.

    Dearden, A., Demiris, Y., Grau, O.: Tracking football player movement from a single moving camera using particle filters. In: Conference on Visual Media Production, pp. 29–37 (2006)

  29. 29.

    Khatoonabadi, S.H., Rahmati, M.: Automatic soccer players tracking in goal scenes by camera motion elimination. Image Vis. Comput. 27(4), 469–479 (2009)

    Article  Google Scholar 

  30. 30.

    Kim, H., Nam, S., Kim, J.: Player segmentation evaluation for trajectory estimation in soccer games. In: Conference on Image and Vision Computing pp. 159–162 (2003)

  31. 31.

    Naemura, M., Fukuda, A., Mizutani, Y., Izumi, Y., Tanaka, Y., Enami, K.: Morphological segmentation of sport scenes using color information. IEEE Trans. Broadcast. 46(3), 181–188 (2000)

    Article  Google Scholar 

  32. 32.

    Pallavi, V., Mukherjee, J., Majumdar, A.K., Sural, S.: Graph-based multiplayer detection and tracking in broadcast soccer videos. IEEE Trans. Multimed. 10(5), 794–805 (2008)

    Article  Google Scholar 

  33. 33.

    Chen, H.T., Chou, C.L., Fu, T.S., Lee, S.Y., Lin, B.S.P.: Recognizing tactic patterns in broadcast basketball video using player trajectory. J. Vis. Commun. Image Represent. 23(6), 932–947 (2012)

    Article  Google Scholar 

  34. 34.

    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Conference on Computer Vision and Pattern Recognition, vol. 2 (1999)

  35. 35.

    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Conference on Computer Vision and Pattern Recognition, pp. I–511 (2001)

  36. 36.

    Liu, J., Tong, X., Li, W., Wang, T., Zhang, Y., Wang, H.: Automatic player detection, labeling and tracking in broadcast soccer video. Pattern Recogn. Lett. 30(2), 103–113 (2009)

    Article  Google Scholar 

  37. 37.

    Yao, J., Odobez, J.M.: Multi-camera multi-person 3d space tracking with mcmc in surveillance scenarios. In: European Conference on Computer Vision—Workshop on Multi Camera and Multi-modal Sensor Fusion Algorithms and Applications (2008)

  38. 38.

    Gedikli, S., Bandouch, J., von Hoyningen-Huene, N., Kirchlechner, B., Beetz, M.: An adaptive vision system for tracking soccer players from variable camera settings. In: International Conference on Computer Vision Systems (2007)

  39. 39.

    Pérez, P., Hue, C., Vermaak, J., Gangnet, M.: Color-based probabilistic tracking. In: European Conference on Computer Vision, pp. 661–675 (2002)

  40. 40.

    Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian bayesian tracking. IEEE Trans. Signal Process. 50(2), 174–188 (2002)

    Article  Google Scholar 

  41. 41.

    Sullivan, J., Carlsson, S.: Tracking and labelling of interacting multiple targets. In: European Conference on Computer Vision, pp. 619–632 (2006)

  42. 42.

    StatDNA. https://www.statdna.com/n. Accessed 1 July 2015

  43. 43.

    Dartfish. http://www.dartfish.com/. Accessed 1 July 2015

  44. 44.

    Borriello, G.: Bayesian filters for location estimation. IEEE Pervasive Comput. 2(3), 24–33 (2003)

    Article  Google Scholar 

  45. 45.

    Chen, Z.: Bayesian filtering: from Kalman filters to particle filters, and beyond. Statistics 182(1), 1–69 (2003)

    Article  Google Scholar 

  46. 46.

    Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000)

    Article  Google Scholar 

  47. 47.

    Kasturi, R., Goldgof, D., Soundararajan, P., Manohar, V., Garofolo, J., Bowers, R., Boonstra, M., Korzhova, V., Zhang, J.: Framework for performance evaluation of face, text, and vehicle detection and tracking in video: data, metrics, and protocol. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 319–336 (2009)

    Article  Google Scholar 

  48. 48.

    Teutsch, M.: Moving Object Detection and Segmentation for Remote Aerial Video Surveillance. KIT SP, Karlsruhe (2015)

    Google Scholar 

  49. 49.

    Montgomery, D.C.: Design and Analysis of Experiments. Wiley, New York (2006)

    Google Scholar 

  50. 50.

    Lucey, P., Oliver, D., Carr, P., Roth, J., Matthews, I.: Assessing team strategy using spatiotemporal data. In: International Conference on Knowledge Discovery and Data Mining, pp. 1366–1374 (2013)

Download references


The authors thank the support of CNPq under Processes 468042/2014-8 and 313163/2014-6, FAPEMIG under Process PPM-00542-15, CEFET-MG, CAPES and Minas Tênis Clube.

Author information



Corresponding author

Correspondence to Flávio L. C. Pádua.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

de Pádua, P.H.C., Pádua, F.L.C., de A. Pereira, M. et al. A vision-based system to support tactical and physical analyses in futsal. Machine Vision and Applications 28, 475–496 (2017). https://doi.org/10.1007/s00138-017-0849-z

Download citation


  • Tactical analysis
  • Physical analysis
  • Futsal
  • Computer vision
  • Player tracking
  • Mobile applications