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A semi-supervised system for players detection and tracking in multi-camera soccer videos

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Abstract

This paper presents a complete, general and modular system which after a simple previous configuration is able to detect and track each player on the court or field. The presented multi-camera system is based on a mono-camera object detection and tracking system originally designed for video surveillance applications. Target sports of the developed system are team sports (e.g., basketball, soccer). The main objective of this paper is to present a semi-supervised system able to detect and track the players in multi-camera sports videos, focusing on the fusion of different tracks of detected blobs in order to match tracks across cameras. The proposed system is simpler than other systems from the state of the art, can operate in real time and has margin to be improved and to reduce supervision adding additional complexity. In addition to the detection and tracking system, an evaluation system has been designed to obtain quantitative results of the system performance.

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Notes

  1. http://www.csse.uwa.edu.au/~pk/research/matlabfns/

  2. In this case the tracking is defined as the set of blobs that is fused with another set of blobs

  3. In the ISSIA soccer dataset, this ID is extracted from the ground truth tracking of each of the 6 cameras. The blob ID for the blobs of a player is the same for the six tracking files.

  4. The LOA, iLOA and eLOA are “classes”, which are instantiated as specific lists (e.g., FCeLOA, RiLOA) during the different fusions performed (see section 5.2).

  5. http://www.issia.cnr.it/htdocs%20nuovo/progetti/bari/soccerdataset.html

  6. A document with the corrections is available in the created web page.

References

  1. Anjum N, Cavallaro A (2009) Trajectory association and fusion across partialy overlapping cameras. AVSS, pp 201–206

  2. Bebie T, Bieri H (1998) SoccerMan-reconstructing soccer games from video sequences. Image processingl 1:898–902

    Google Scholar 

  3. Choi S, Seo Y, Kim H, Hong KS (1997) Where are the ball and players? Soccer game analysis with colorbased tracking and image mosaic. In: Proc. of ICIAP, pp 196–203

  4. D’Orazio T, Leo M, Mosca N, Spagnolo P, Mazzeo PL (2009) A semi-automatic system for ground truth generation of soccer video sequences. AVSS, pp 559–564

  5. de Meneses YL, Roduit P, Luisier F, Jacot J (2005) Trajectory analysis for sport and video surveillance. Electron Lett Comput Vis Image Anal 5(3):148–156

    Google Scholar 

  6. Du W, Hayet JB, Piater J, Verly J (2006) Collaborative multi-camera tracking of athletes in team sports. Workshop on Computer Vision Based Anal in Sports Environments (CVBASE), pp 2–13

  7. Figueroa PJ, Leite NJ, Barros RML (2006) Tracking soccer players aiming their kinematical motion analysis. Trans Comput Vis Image Underst 101(2):122–135

    Google Scholar 

  8. Figueroa P, Leite N, Barros R, Cohen I, Medioni G (2004) Tracking soccer players using the graph representation. In: Proc. of ICPR 4:787–790

  9. Hartley R, Zisserman A (2003) A multiple view geometry in computer vision. Cambridge University Press

  10. Huang Y, Llach J, Bhagavathy S (2007) Players and ball detection in soccer videos based on color segmentation and shape analysis. Lecture Notes in Computer Science 4577:416–425

    Google Scholar 

  11. Junejo IN, Foroosh H (2007) Trajectory rectification and path modeling for video surveillance. In: Proc. of ICCV, pp 1–7

  12. Kang J, Cohen I, Medioni G (2004) Tracking people in crowded scenes across multiple cameras. In: Proc. of ACCV

  13. Kayumbi G, Anjum N, Cavallaro A (2008) Global trajectory reconstruction from distributed visual sensors. In: Proc. of ICDSC, pp 1–8

  14. Kayumbi G, Mazzeo PL, Spagnolo P, Taj M, Cavallaro A (2008) Distributed visual sensing for virtual top-view trajectory generation in football videos. In: Proc. of CIVR

  15. Martín R, Martínez JM (2013) An automatic system for sports analytics in multi-camera tennis videos. In: Proc. of AMMDS-AVSS (in press)

  16. Misu T, Gohshi S, Izumi Y, Fujita Y, Naemura M (2004) Robust tracking of athletes using multiple features of multiple views. In: Proc. of WSCG, pp 285–292

  17. Nummiaro K, Koller-Meier E, Svoboda T, Roth D, Van Gool J-L (2003) Color-based object tracking in multi-camera environments. In: Proc. of DAGM, pp 591–599

  18. Poppe C, Bruyne SD, Verstockt S, de Walle RV (2010) Multi-camera analysis of soccer sequences. In: Proc. of AVSS, pp 26–31

  19. Sachiko I, Hideo S (2004) Parallel tracking of all soccer players by integrating detected positions in multiple view images. In: Proc. of ICPR

  20. SanMiguel JC, Martínez JM (2012) A semantic-based probabilistic approach for real-time video event recognition. Comp Vis Image Underst 116(9):937–952

    Article  Google Scholar 

  21. Sheikh YA, Shah M (2008) Trajectory association across multiple airborne cameras. Trans Pattern Anal Mach Intell 30(2):361–367

    Article  Google Scholar 

  22. Taj M, Cavallaro A (2009) Multi-camera track-before-detect. In: Proc. of ICDSC

  23. Tong X, et al (2004) An effective and fast soccer ball detection and tracking method. In: Proc of ICPR 4:795–798

  24. Xinguo Y, Farin D (2005) Current and emerging topics in sports video processing. In: Proc. of ICME

  25. Xu M, Orwell J, Jones G (2004) Tracking football players with multiple cameras. In: Proc. of ICIP 5:2909–2912

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Acknowledgments

This work has been partially supported by the Spanish Government (TEC2011-25995).

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Correspondence to Rafael Martín.

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Martín, R., Martínez, J.M. A semi-supervised system for players detection and tracking in multi-camera soccer videos. Multimed Tools Appl 73, 1617–1642 (2014). https://doi.org/10.1007/s11042-013-1659-6

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