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Video Analytics for Business Intelligence pp 43–67Cite as

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

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 409))

Abstract

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.

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Authors and Affiliations

  1. AIT Austrian Institute of Technology, Donau-City-Straße 1, 1220, Vienna, Australia

    Cristina Picus, Roman Pflugfelder & Branislav Micusik

Authors
  1. Cristina Picus
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  2. Roman Pflugfelder
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  3. Branislav Micusik
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Corresponding author

Correspondence to Cristina Picus .

Editor information

Editors and Affiliations

  1. Philips Research, High Tech Campus 36, Eindhoven, 5656 AE, Netherlands

    Caifeng Shan

  2. Mitsubishi Electric Research Laboratorie, Broadway 201, Cambridge, 02139, USA

    Fatih Porikli

  3. Dept. Computer Science, Queen Mary University of London, Mile End Rd., London, E1 4NS, United Kingdom

    Tao Xiang

  4. Dept. Computer Science, Queen Mary University of London, Mile End Rd., London, E1 4NS, United Kingdom

    Shaogang Gong

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© 2012 Springer Berlin Heidelberg

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Picus, C., Pflugfelder, R., Micusik, B. (2012). Auto-calibration of Non-overlapping Multi-camera CCTV Systems. In: Shan, C., Porikli, F., Xiang, T., Gong, S. (eds) Video Analytics for Business Intelligence. Studies in Computational Intelligence, vol 409. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28598-1_2

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  • DOI: https://doi.org/10.1007/978-3-642-28598-1_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28597-4

  • Online ISBN: 978-3-642-28598-1

  • eBook Packages: EngineeringEngineering (R0)

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