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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)

Abstract

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.

Keywords

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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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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