Towards Feature-Based Situation Assessment for Airport Apron Video Surveillance

  • Ralf Dragon
  • Michele Fenzi
  • Wolf Siberski
  • Bodo Rosenhahn
  • Jörn Ostermann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7474)


We present a feature-based surveillance pipeline which, in contrast to traditional image-based methods, allows to learn a detailed description of the observed background as well as of foreground objects. The pipeline consists of motion segmentation of feature trajectories and subsequent tracking-by-recognition with updates. Furthermore, 3D object representations are learned in order to extract the 3D object pose of a later object recognition. Finally, we show how such sufficiently reliable information is inputted into a reasoning system comparing actual and nominal condition of an airport apron. By this, automatic situation assessment becomes possible in a manageable and reliable way.


Video Surveillance Foreground Object Reasoning System Sift Feature Structure From Motion 
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 2012

Authors and Affiliations

  • Ralf Dragon
    • 1
  • Michele Fenzi
    • 1
  • Wolf Siberski
    • 2
  • Bodo Rosenhahn
    • 1
  • Jörn Ostermann
    • 1
  1. 1.Institut für InformationsverarbeitungGermany
  2. 2.L3SLeibniz Universität HannoverGermany

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