Comparative Study of People Detection in Surveillance Scenes (original) (raw)

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.

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Authors and Affiliations

  1. Institut National Polytechnique de Grenoble, Laboratory GRAVIR, INRIA Rhone-Alpes, France
    A. Negre, H. Tran, N. Gourier, D. Hall, A. Lux & J. L. Crowley

Authors

  1. A. Negre
  2. H. Tran
  3. N. Gourier
  4. D. Hall
  5. A. Lux
  6. J. L. Crowley

Editor information

Editors and Affiliations

  1. Hong Kong University of Science and Technology,
    Dit-Yan Yeung
  2. Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
    James T. Kwok
  3. Instituto de Telecomunicações, Instituto Superior Técnico, Lisbon, Portugal
    Ana Fred
  4. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy
    Fabio Roli
  5. Faculty of Electrical Engineering, Mathematics and Computer Science, Information and Communication Theory Group, Delft University of Technology, Delft, The Netherlands
    Dick de Ridder

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© 2006 Springer-Verlag Berlin Heidelberg

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Negre, A., Tran, H., Gourier, N., Hall, D., Lux, A., Crowley, J.L. (2006). Comparative Study of People Detection in Surveillance Scenes. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921\_10

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