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Hierarchical Architecture for Robust People Detection by Fusion of Infrared and Visible Video

  • José Carlos CastilloEmail author
  • Juan Serrano-Cuerda
  • Antonio Fernández-Caballero
  • Arturo Martínez-Rodrigo
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 616)

Abstract

Robust people detection systems are nowadays using heterogeneous cameras. This paper proposes an hierarchical architecture which is focused on robustly detecting people by fusion of infrared and visible video. The architecture covers all levels provided by the INT\(^3\)-Horus framework, initially designed to perform monitoring and activity interpretation tasks. Indeed, INT\(^3\)-Horus is used as the development environment where the approach starts with image segmentation in both infrared and visible spectra. Then, the results are fused to enhance the overall detection performance.

Keywords

Fusion Level Hierarchical Architecture Human Detection Activity Interpretation People Detection 
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.

Notes

Acknowledgments

This work was partially supported by Spanish Ministerio de Economía y Competitividad/FEDER under TIN2013-47074-C2-1-R grant.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • José Carlos Castillo
    • 1
    Email author
  • Juan Serrano-Cuerda
    • 2
  • Antonio Fernández-Caballero
    • 3
  • Arturo Martínez-Rodrigo
    • 4
  1. 1.Department of Systems Engineering and AutomaticUniversity Carlos III of MadridMadridSpain
  2. 2.Instituto de Investigación en InformáticaUniversidad de Castilla-La ManchaAlbaceteSpain
  3. 3.Departamento der Sistemas InformáticaUniversidad de Castilla-La ManchaAlbaceteSpain
  4. 4.Instituto de Tecnologías AudiovisualesUniversidad de Castilla-La ManchaAlbaceteSpain

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