An Area-Based Decision Rule for People-Counting Systems

  • Hyun Hee Park
  • Hyung Gu Lee
  • Seung-In Noh
  • Jaihie Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)


In this paper, we propose an area-based decision rule for counting the number of people that pass through a given ROI (Region of Interest). This decision rule divides obtained images into 72 sectors and the size of the person is trained to calculate the mean and variance values for each divided sector. These values are then stored in table form and can be used to count people in the future. We also analyze various movements that people perform in the real world. For instance, during busy hours, people frequently merge and split with each other. Therefore, we propose a system for counting the number of passing people more accurately and a way of discovering the direction of their paths.


Motion Vector Training Image Stereo Camera Reference Background Counting Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Huang, D., Chow, T.W.S., Chau, W.N.: Neural network based system for counting people. In: IECON 2002 [IEEE 2002 28th Annual Conference of the Industrial Electronics Society], November 5-8, vol. 3, pp. 2197–2201 (2002)Google Scholar
  2. 2.
    Terada, K., Yoshida, D., Oe, S., Yamaguchi, J.: A method of counting the passing people by using the stereo images. In: Proceedings of the International Conference on Image Processing, ICIP 1999, October 24-28, vol. 2, pp. 338–342 (1999)Google Scholar
  3. 3.
    Terada, K., Yoshida, D., Oe, S., Yamaguchi, J.: A counting method of the number of passing people using a stereo camera. In: Proceedings of the 25th Annual Conference of the IEEE, Industrial Electronics Society, IECON 1999, December 29, vol. 3, pp. 1318–1323 (1999)Google Scholar
  4. 4.
    Chen, T.-H.: An automatic bi-directional passing-people counting method based on color image processing. In: Proceedings IEEE 37th Annual 2003 International Carnahan Conference on Security Technology, October 14-16, pp. 200–207 (2003)Google Scholar
  5. 5.
    Terada, K., Umemoto, T.: Observing passing people by using fiber grating vision sensor. In: Proceedings of the 2004 IEEE International Conference on Control Applications, September 2-4, vol. 2, pp. 1112–1117 (2004)Google Scholar
  6. 6.
    Zhang, X., Sexton, G.: Automatic pedestrian counting using image processing techniques. Electronics Letters 31(11), 863–865 (1995)CrossRefGoogle Scholar
  7. 7.
    Sexton, G., Zhang, X.: Automatic human head location for pedestrian counting. Image Processing for Security Applications (Digest No: 1997/074), IEE Colloquium, 10/1–10/3 (March 10, 1997)Google Scholar
  8. 8.
    Zhang, X., Sexton, G.: A new method for pedestrian counting. In: Fifth International Conference on Image Processing and its Applications, July 4-6, pp. 208–212 (1995)Google Scholar
  9. 9.
    Sexton, G., Zhang, X., Redpath, G., Greaves, D.: Advances in automated pedestrian counting. In: European Convention on Security and Detection, May 16-18, pp. 106–110 (1995)Google Scholar
  10. 10.
    Zang, Q., Klette, R.: Robust background subtraction and maintenance. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, August 23-26, vol. 2, pp. 90–93 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hyun Hee Park
    • 1
  • Hyung Gu Lee
    • 1
  • Seung-In Noh
    • 2
  • Jaihie Kim
    • 1
  1. 1.Department of Electrical and Electronic EngineeringYonsei University, Biometrics Engineering Research Center(BERC)Republic of Korea
  2. 2.Samsung ElectronicsYeongtong-gu, Suwon-city, Gyeonggi-doRepublic of Korea

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