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From Single Cameras to the Camera Network: An Auto-Calibration Framework for Surveillance

  • Cristina Picus
  • Branislav Micusik
  • Roman Pflugfelder
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6376)

Abstract

This paper presents a stratified auto-calibration framework for typical large surveillance set-ups including non-overlapping cameras. The framework avoids the need of any calibration target and purely relies on visual information coming from walking people. Since in non-overlapping scenarios there are no point correspondences across the cameras the standard techniques cannot be employed. We show how to obtain a fully calibrated camera network starting from single camera calibration and bringing the problem to a reduced form suitable for multi-view calibration. We extend the standard bundle adjustment by a smoothness constraint to avoid the ill-posed problem arising from missing point correspondences. The proposed framework optimizes the objective function in a stratified manner thus suppressing the problem of local minima. Experiments with synthetic and real data validate the approach.

Keywords

Camera View Camera Parameter Single Camera Bundle Adjustment 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 2010

Authors and Affiliations

  • Cristina Picus
    • 1
  • Branislav Micusik
    • 1
  • Roman Pflugfelder
    • 1
  1. 1.Safety and Security DepartmentAIT Austrian Institute of Technology 

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