Robust Segmentation Process to Detect Incidents on Highways

  • Gonçalo Monteiro
  • João Marcos
  • Miguel Ribeiro
  • Jorge Batista
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5112)


In this paper a robust segmentation process for detecting incidents on highways is presented. This segmentation process is based on background subtraction and uses an efficient background model initialisation and update to work 24/7. A cross-correlation based shadow detection is also used for minimising ghosts. It is also proposed a stopped vehicle detection system based on the pixel history cache. This methodology has proved to be quite robust in terms of different weather conditions, lighting and image quality. Some experiments carried out on some highway scenarios demonstrate the robustness of the proposed solution.


Background Model Segmentation Process Lighting Variation Foreground Pixel Vehicle Detection 
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 2008

Authors and Affiliations

  • Gonçalo Monteiro
    • 1
  • João Marcos
    • 1
  • Miguel Ribeiro
    • 1
  • Jorge Batista
    • 1
  1. 1.Institute for System and Robotics Dep. of Electrical Engineering and ComputersUniversity of CoimbraPortugal

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