A Case Study on Logging Visual Activities: Chess Game

  • Şükrü Ozan
  • Şevket Gümüştekin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3949)


Automatically recognizing and analyzing visual activities in complex environments is a challenging and open-ended problem. In this study this task is performed in a chess game scenario where the rules, actions and the environment are well defined. The purpose here is to detect and observe a FIDE (Fédération International des Ėchecs) compatible chess board, generating a log file of the moves made by human players. A series of basic image processing operations have been applied to perform the desired task. The first step of automatically detecting a chess board is followed by locating the positions of the pieces. After the initial setup is established every move made by a player is automatically detected and verified. Intel® Open Source Computer Vision Library (OpenCV) is used in the current software implementation.


Difference Image Content Base Image Retrieval Move Table Video Annotation Projected View 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Koprinska, I., Carrato, S.: Temporal video segmentation: A survey. Signal Processing: Image Communication 16(5), 477–500 (2001)Google Scholar
  2. 2.
    Smoulders, A.W.M., Worring, M., Santini, S., Gupta, A., Jain, R.: Content based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Machine Intell. 22, 1349–1380 (2000)CrossRefGoogle Scholar
  3. 3.
    Dorado, A., Calic, J., Izquierdo, E.: A rule-based video annotation system. IEEE Transactions on Circuits and Systems for Video Technology 14(5), 622–633 (2004)CrossRefGoogle Scholar
  4. 4.
    Brunelli, R., Mich, O., Modena, C.M.: A Survey on the Automatic Indexing of Video Data. Journal of Visual Communication and Image Representation 10(2), 78–112 (1999)CrossRefGoogle Scholar
  5. 5.
    Assfalg, J., Bertini, M., Colombo, C., Bimbo, A.D., Nunziati, W.: Semantic annotation of soccer videos: automatic highlights identification. Computer Vision and Image Understanding 92(2-3), 285–305 (2003)CrossRefGoogle Scholar
  6. 6.
    Ekin, A., Tekalp, A.M., Mehrotra, R.: Automatic soccer video analysis and summarization. IEEE Transactions on Image Processing 12(7), 796–807 (2003)CrossRefGoogle Scholar
  7. 7.
    Campbell, M., Hoane, A.J., Hsu, F.H.: Deep Blue. Artificial Intelligence 134(1-2), 57–83 (2002)CrossRefMATHGoogle Scholar
  8. 8.
    Baird, H.S., Thompson, K.: Reading chess. IEEE Trans. Pattern Anal. Machine Intell. 12(6), 552–559 (1990)CrossRefGoogle Scholar
  9. 9.
    Groen, F.C.A., Den Boer, G.A., Van Inge, A., Stam, R.: A chess-playing robot: lab course in robot sensor integration. IEEE Transactions on Instrumentation and Measurement 41(6), 911–914 (1992)CrossRefGoogle Scholar
  10. 10.
    Uyar, E., Gümüştekin, Ş., Ozan, Ş., Çetin, L.: A Computer Controlled Vision Oriented Robot Manipulator for Chess Game. In: Proc. of Workshop on Research and Education in Control and Signal Processing, REDISCOVER 2004, Cavtat, Croatia (2004)Google Scholar
  11. 11.
  12. 12.
    Gonzales, R.C., Woods, R.E.: Digital Image Processing. Addison-Wesley, Reading (1993)Google Scholar
  13. 13.
    Trucco, E., Verri, A.: Introductory Techniques for 3D Computer Vision. Prentice Hall, Englewood Cliffs (1998)Google Scholar
  14. 14.
    Zimmermann, J.Z.: Fuzzy Set Theory and its Applications. Kluwer Academic Publishers, Dordrecht (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Şükrü Ozan
    • 1
  • Şevket Gümüştekin
    • 1
  1. 1.Izmir Institute of TechnologyUrla IzmirTurkey

Personalised recommendations