Skip to main content

Spatio-Temporal Decomposition of Sport Events for Video Indexing

  • Conference paper
  • First Online:
  • 1185 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2728))

Abstract

In this paper we present a robust technique for summarizing sport video sequences. Unlike dense optical flow and parametric methods, we develop a semi-automatic application that uses geometrical information, such as straight lines, extracted from sequences of images. This information is used to compute the homographies between consecutive frames. This estimation yields a manner of synthesizing a high resolution image of the background plus the field-centered trajectories of the moving objects onto it.

Work supported by CICYT grant TEL99-1206-C02-02

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. M. J. Black and P. Anandan. A framework for the robust estimation of optical flow. In Fourth International Conf. on Computer Vision, pages 231–236, 1993.

    Google Scholar 

  2. M. J. Black and P. Anandan. The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields. Computer Vision and Image Understanding: CVIU, 63(1):75–104, 1996.

    Article  Google Scholar 

  3. J.-Y. Bouguet. Pyramidal implementation of the lucas kanade feature tracker description of the algorithm. Microprocessor Research Labs, 2000.

    Google Scholar 

  4. D.A. Forsyth and J. Ponce. Computer Vision: A Modern Approach. Prentice Hall, 2002.

    Google Scholar 

  5. G.H. Golub and C. F. V. Loan. Matrix Computations. The John Hopkins University Press, third edition, 1996.

    Google Scholar 

  6. R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2000.

    Google Scholar 

  7. MPEG Requirements Group. MPEG-7 context, objectives and technical roadmap, doc. ISO/IEC/JTC1/SC29/WG11 N2729, Seoul meeting, March 1999.

    Google Scholar 

  8. P. Salembier, R. Qian, N. O’Connor, P. Correia, I. Sezan, and P. van Beek. Description schemes for video programs, users and devices. Signal Processing: Image Communication., 16:211–234, 2000.

    Article  Google Scholar 

  9. H. Shum and R. Szeliski. Panoramic image mosaics. Microsoft Research Technical Report MSR-TR-97-23, 1997.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Barceló, L., Orriols, X., Binefa, X. (2003). Spatio-Temporal Decomposition of Sport Events for Video Indexing. In: Bakker, E.M., Lew, M.S., Huang, T.S., Sebe, N., Zhou, X.S. (eds) Image and Video Retrieval. CIVR 2003. Lecture Notes in Computer Science, vol 2728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45113-7_43

Download citation

  • DOI: https://doi.org/10.1007/3-540-45113-7_43

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40634-1

  • Online ISBN: 978-3-540-45113-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics