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Camera calibration using court models for real-time augmenting soccer scenes

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Abstract

In this paper, we present a procedure to estimate the position, orientation and focal length of a camera in a soccer field. These parameters are then used in real-time overlay of graphics on a soccer pitch. The method uses court model composed by arcs and lines. A means of automatically initializing the tracking process is also presented which uses Hough transform with a combination of a non-linear least squares optimization method. For the tracking of camera parameters, two cases arise: the center of the pitch and the 18 m area. A combination of automatic court model recognition with the Kanade-Lucas-Tomasi (KLT) algorithm is also used.

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References

  1. Assfalg J, Bertini M, Colombo C, Bimbo AD, Nunziati W (2003) Semantic annotation of soccer videos: automatic highlights identification. Comput Vis Image Underst 92(2–3):285–305

    Article  Google Scholar 

  2. Ekin A, Tekalp AM (2003) Automatic soccer video analysis and summarization, in SPIE Storage and Retrieval for Media Database IV. pp 339–350.

  3. Farin D, Krabbe S, de With PHN, Effelsberg W (2004) Robust camera calibration for sport videos using court models. in SPIE Storage and Retrieval Methods and Applications for Multimedia

  4. Gong Y, Chua HC, Lim TS (1995) An automatic video parser for TV soccer games. The Second Asian Conference on Computer Vision 2:509–513

    Google Scholar 

  5. Kim H, Hong K (2001) Robust image mosaicing of soccer videos using self-calibration and line tracking. Pattern Anal Appl 4(1):9–19

    Article  MathSciNet  MATH  Google Scholar 

  6. Le Troter A, Bulot R, Boï J-M, Sequeira J (2005) Arc of ellipse detection for video image registration. in: SiPS, Athènes, Grèce

  7. Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. International Joint Conference on Artificial Intelligence. pp 674–679

  8. Slama C (1980) Manual of photogrammetry. American society of photogrammetry, 4th edn. Falls Church, VA, USA

    Google Scholar 

  9. Shi J, Tomasi C (1994) Good features to track. IEEE Conference on Computer Vision and Pattern Recognition 593–600

  10. Thomas G (2006) Real-time camera pose estimation for augmenting sports scenes. In CVMP

  11. Tomasi C, Kanade T (1991) Detection and tracking of point features. Carnegie Mellon University Technical Report CMU-CS-91–132

  12. Tsai RY (1987) A versatile camera calibration technique for high-accuracy 3D machine vision metrology usin off the-shelf TV cameras and lenses. IEEE J Robot Autom 3(4):323–344

    Article  Google Scholar 

Download references

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Correspondence to Imed Jabri.

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battikh, T., Jabri, I. Camera calibration using court models for real-time augmenting soccer scenes. Multimed Tools Appl 51, 997–1011 (2011). https://doi.org/10.1007/s11042-009-0434-1

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  • DOI: https://doi.org/10.1007/s11042-009-0434-1

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