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Finding Camera Overlap in Large Surveillance Networks

  • Anton van den Hengel
  • Anthony Dick
  • Henry Detmold
  • Alex Cichowski
  • Rhys Hill
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4843)

Abstract

Recent research on video surveillance across multiple cameras has typically focused on camera networks of the order of 10 cameras. In this paper we argue that existing systems do not scale to a network of hundreds, or thousands, of cameras. We describe the design and deployment of an algorithm called exclusion that is specifically aimed at finding correspondence between regions in cameras for large camera networks. The information recovered by exclusion can be used as the basis for other surveillance tasks such as tracking people through the network, or as an aid to human inspection. We have run this algorithm on a campus network of over 100 cameras, and report on its performance and accuracy over this network.

Keywords

Foreground Object Multiple Camera Camera Network Occupancy Data Surveillance Task 
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 2007

Authors and Affiliations

  • Anton van den Hengel
    • 1
  • Anthony Dick
    • 1
  • Henry Detmold
    • 1
  • Alex Cichowski
    • 1
  • Rhys Hill
    • 1
  1. 1.School of Computer Science, University of Adelaide, Adelaide, 5005Australia

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