Scene Understanding for Auto-Calibration of Surveillance Cameras

  • Lucas Teixeira
  • Fabiola Maffra
  • Atta Badii
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8888)


In the last decade, several research results have presented formulations for the auto-calibration problem. Most of these have relied on the evaluation of vanishing points to extract the camera parameters. Normally vanishing points are evaluated using pedestrians or the Manhattan World assumption i.e. it is assumed that the scene is necessarily composed of orthogonal planar surfaces. In this work, we present a robust framework for auto-calibration, with improved results and generalisability for real-life situations. This framework is capable of handling problems such as occlusions and the presence of unexpected objects in the scene. In our tests, we compare our formulation with the state-of-the-art in auto-calibration using pedestrians and Manhattan World-based assumptions. This paper reports on the experiments conducted using publicly available datasets; the results have shown that our formulation represents an improvement over the state-of-the-art.


Little Mean Square Camera Calibration Surveillance Camera Little Mean Square Algorithm Pedestrian 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 International Publishing Switzerland 2014

Authors and Affiliations

  • Lucas Teixeira
    • 1
  • Fabiola Maffra
    • 1
  • Atta Badii
    • 1
  1. 1.Intelligent Systems Research LaboratoryUniversity of ReadingUK

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