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Comparative Study of People Detection in Surveillance Scenes

  • A. Negre
  • H. Tran
  • N. Gourier
  • D. Hall
  • A. Lux
  • J. L. Crowley
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)

Abstract

We address the problem of determining if a given image region contains people or not, when environmental conditions such as viewpoint, illumination and distance of people from the camera are changing. We develop three generic approaches to discriminate between visual classes: ridge-based structural models, ridge-normalized gradient histograms, and linear auto-associative memories. We then compare the performance of these approaches on the problem of people detection for 26 video sequences taken from the CAVIAR database.

Keywords

Sift Descriptor Connexion Matrix Main Ridge People Detection Ridge Point 
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 2006

Authors and Affiliations

  • A. Negre
    • 1
  • H. Tran
    • 1
  • N. Gourier
    • 1
  • D. Hall
    • 1
  • A. Lux
    • 1
  • J. L. Crowley
    • 1
  1. 1.Institut National Polytechnique de Grenoble, Laboratory GRAVIRINRIA Rhone-AlpesFrance

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