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Motion Detection in Complex and Dynamic Backgrounds

  • Daeyong Park
  • Junbeom Kim
  • Jaemin Kim
  • Seongwon Cho
  • Sun-Tae Chung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4319)

Abstract

For the detection of moving objects, background subtraction methods are widely used. In case the background changes, we need to update the background in real-time for the reliable detection of foreground objects. An adaptive Gaussian mixture model (GMM) combined with probabilistic learning is one of the most popular methods for the real-time update of the complex and dynamic background. However, the probabilistic learning approach does not work well in high traffic regions. In this paper, we classify each pixel into four different types: still background, dynamic background, moving object, and still object, and update the background model based on the classification. For the classification, we analyze a sequence of frame differences at each pixel and its neighborhood. We experimentally show that the proposed method learn complex and dynamic backgrounds in high traffic regions more reliably, compared with traditional methods.

Keywords

Gaussian Mixture Model Background Model Motion Detection Foreground Object Dynamic Background 
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

  • Daeyong Park
    • 1
  • Junbeom Kim
    • 1
  • Jaemin Kim
    • 1
  • Seongwon Cho
    • 1
  • Sun-Tae Chung
    • 2
  1. 1.School of Electronics and Electrical EngineeringHongik UniversitySeoulKorea
  2. 2.School of Electronics EngineeringSoongsil University 

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