Skip to main content
SpringerLink
Log in

Grass Field Segmentation, the First Step Toward Player Tracking, Deep Compression, and Content Based Football Image Retrieval

  • Conference paper
Image Analysis and Recognition (ICIAR 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3211))

Included in the following conference series:

  • 496 Accesses

Abstract

In this paper, a method is presented which can be used for the segmentation of grass field in video images taken from football matches. Grass field is a green and nearly soft region. Therefore color and texture are two suitable features which can be used to describe it. As HSI color space is more stable against illumination changes in comparison with other color spaces, the hue is selected as color feature.

Sub-band images containing high frequency information in horizontal, vertical and diagonal directions that are obtained by applying wavelet transform on image intensity have been used for texture description. Classification of grass and non-grass fields is done using an MLP classifier. The results revealed that the proposed method is able to recognize grass and non-grass samples accurately.

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

Access this chapter

Log in via an institution
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Seo, Y., Choi, S., Kim, H., Hong, K.S.: Where are the ball and players?: Soccer Game Analysis with Color-based Tracking and Image Mosaic. In: Proceedings of Int. Conference on Image Analysis and Processing (ICIAP), pp. 196–203 (1997)

    Google Scholar 

  2. Utsumi, O., Miura, K., Ide, I., Sakai, S., Tanaka, H.: An Object Detection Method for Describing Soccer Games from Video. In: Proceedings of IEEE Int. Conference on Multimedia and Expo. (ICME), vol. 1, pp. 45–48 (2002)

    Google Scholar 

  3. Sudhir, G., Lee, J.C.M., Jain, A.K.: Automatic Classification of Tennis Video for Highlevel Content-based Retrieval. In: International Workshop on Content-Based Access of Image and Video Databases (CAIVD), pp. 81–90 (1998)

    Google Scholar 

  4. Hua, W., Han, M., Gong, Y.: Baseball Scene Classification Using Multimedia Features. In: Proceedings of IEEE International Conference on Multimedia and Expo., vol. 1, pp. 821–824 (2002)

    Google Scholar 

  5. Xu, P., Xie, L., Chang, S.F., Divakaran, A., Vetro, A., Sun, H.: Algorithms and System for Segmentation and Structure Analysis in Soccer Video. In: Proceedings of IEEE Int. Conference on Multimedia and Expo. (ICME), pp. 928–931 (2001)

    Google Scholar 

  6. Pal, N.R., Pal, S.K.: A Review on Image Segmentation Technique. Pattern Recognition Letters 26, 1277–1294 (1993)

    Google Scholar 

  7. Shi, J., Malik, J.: Normalized Cuts and Image Segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 22, 888–905 (2000)

    Article  Google Scholar 

  8. Bleau, A., Joshua Leon, L.: Watershed-Based Segmentation and Region Merging. Computer Vision and Image Understanding 77, 317–370 (2000)

    Article  Google Scholar 

  9. Marroquin, J.L., Santana, E.A., Botello, S.: Hidden Markov Measure Field Models for Image Segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 25, 1380–1387 (2003)

    Article  Google Scholar 

  10. Bhalerao, A., Wilson, R.: Unsupervised Image Segmentation Combining Region and Boundary Estimation. IVC 19, 353–368 (2001)

    Google Scholar 

  11. Finlayson, G., Schaefer, G.: Hue That is Invariant to Brightness and Gamma. In: Proceedings of British Machine Vision Conference, pp. 303–312 (2000)

    Google Scholar 

  12. Buluswar, S.D., Draper, B.A.: Color Models for Outdoor Machine Vision. Computer Vision and Image Understanding 85, 71–99 (2002)

    Article  MATH  Google Scholar 

  13. Lin, H.C., Chiu, C.Y., Yang, S.N.: Finding Textures by Textual Descriptions, Visual Examples and Relevance Feedbacks. Pattern Recognition Letters 24, 2255–2267 (2003)

    Article  MATH  Google Scholar 

  14. Zhu, S.C.: Statistical Modeling and Conceptualization of Visual Patterns. IEEE Trans. on Pattern Analysis and Machine Intelligence 25, 691–712 (2003)

    Article  Google Scholar 

  15. Pentland, A.: Fractal-Based Description of Natural Scenes. IEEE Trans. on Pattern Analysis and Machine Intelligence 6, 661–674 (1984)

    Article  Google Scholar 

  16. Azencott, R., Wang, J.P., Younes, L.: Texture Classification Using Windowed Fourier Filters. IEEE Trans. on Pattern Analysis and Machine Intelligence 19, 148–153 (1997)

    Article  Google Scholar 

  17. Abramovich, F., Bailey, T., Sapatinas, T.: Wavelet Analysis and its Statistical Applications. Journal of the Royal Statistical Society (JRSSD) 48, 1–30 (2000)

    Article  Google Scholar 

  18. Arivazhagan, S., Ganesan, L.: Texture Segmentation using Wavelet Transform. Pattern Recognition Letters 24, 3197–3203 (2003)

    Article  Google Scholar 

  19. ISO-11172-2: Generic Coding of Moving Pictures and Associated Audio (MPEG-1)

    Google Scholar 

  20. Fasulo, D.: An Analysis of Recent Work on Clustering Algorithms. Technical Report 01-03-2, University of Washington (April 1999)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Electrical Eng, Azad University, Central Tehran Branch, Tehran, Iran

    Kaveh Kangarloo

  2. Dept. of Electrical Eng, Azad University, Science and Research unit, Tehran, Iran

    Kaveh Kangarloo

  3. Dept. of Electrical Eng, Tarbiat Modarres University, Tehran, Iran

    Ehsanollah Kabir

Authors
  1. Kaveh Kangarloo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ehsanollah Kabir
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. FEUP - Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

  2. Electrical and Computer Engineering Department, University of Waterloo,  

    Mohamed Kamel

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kangarloo, K., Kabir, E. (2004). Grass Field Segmentation, the First Step Toward Player Tracking, Deep Compression, and Content Based Football Image Retrieval. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30125-7_101

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30125-7_101

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23223-0

  • Online ISBN: 978-3-540-30125-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation