Auto-calibration of Non-overlapping Multi-camera CCTV Systems

  • Cristina Picus
  • Roman Pflugfelder
  • Branislav Micusik
Part of the Studies in Computational Intelligence book series (SCI, volume 409)


Deployment of existing vision approaches in camera networks for applications such as human tracking show a large gap between user expectation and current results. Calibrated cameras could push these approaches closer to applicability, as physical constraints greatly complement the ill-posed acquisition process. Calibrated cameras promise also new applications as spatial relationships among cameras and the environment capture additional information. However, a convenient calibration is still a challenge on its own. This paper presents a novel calibration framework for large networks including non-overlapping cameras. The framework purely relies on visual information coming from walking people. Since non-overlapping scenarios make point correspondences impossible, time constancy of a person’s motion introduces the missing complementary information. The framework obtains calibrated cameras starting from single camera calibration thereby bringing the problem to a reduced form suitable for multi-view calibration. It extends the standard bundle adjustment by a smoothness constraint to avoid the ill-posed problem arising from missing point correspondences. The stratified optimization suppresses the danger to get stuck in local minima. Experiments with synthetic and real data validate the approach.


Camera Calibration Camera Parameter Bundle Adjustment Human Detection Smoothness Constraint 
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 Berlin Heidelberg 2012

Authors and Affiliations

  • Cristina Picus
    • 1
  • Roman Pflugfelder
    • 1
  • Branislav Micusik
    • 1
  1. 1.AIT Austrian Institute of TechnologyViennaAustralia

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