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Multi-sensor Fire Detection by Fusing Visual and Non-visual Flame Features

  • Steven Verstockt
  • Alexander Vanoosthuyse
  • Sofie Van Hoecke
  • Peter Lambert
  • Rik Van de Walle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6134)

Abstract

This paper proposes a feature-based multi-sensor fire detector operating on ordinary video and long wave infrared (LWIR) thermal images. The detector automatically extracts hot objects from the thermal images by dynamic background subtraction and histogram-based segmentation. Analogously, moving objects are extracted from the ordinary video by intensity-based dynamic background subtraction. These hot and moving objects are then further analyzed using a set of flame features which focus on the distinctive geometric, temporal and spatial disorder characteristics of flame regions. By combining the probabilities of these fast retrievable visual and thermal features, we are able to detect the fire at an early stage. Experiments with video and LWIR sequences of fire and non-fire real case scenarios show good results and indicate that multi-sensor fire analysis is very promising.

Keywords

False Alarm Moving Object Fire Detection Spatial Disorder Real Case Scenario 
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 2010

Authors and Affiliations

  • Steven Verstockt
    • 1
    • 2
  • Alexander Vanoosthuyse
    • 2
  • Sofie Van Hoecke
    • 2
  • Peter Lambert
    • 1
  • Rik Van de Walle
    • 1
  1. 1.Department of Electronics and Information Systems, Multimedia LabGhent University - IBBTLedeberg-GhentBelgium
  2. 2.University College West Flanders, Ghent University AssociationKortrijkBelgium

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