Multi-modal time-of-flight based fire detection

  • Authors:

  • Steven Verstockt;Sofie Hoecke;Pieterjan Potter;Peter Lambert;Charles Hollemeersch;Bart Sette;Bart Merci;Rik Walle

  • Affiliations:

  • ELIS Department, Multimedia Lab, Ghent University --- IBBT, Ledeberg-Ghent, Belgium 9050 and ELIT Lab, University College West Flanders, Ghent University Association, Kortrijk, Belgium 8500;ELIT Lab, University College West Flanders, Ghent University Association, Kortrijk, Belgium 8500;ELIS Department, Multimedia Lab, Ghent University --- IBBT, Ledeberg-Ghent, Belgium 9050;ELIS Department, Multimedia Lab, Ghent University --- IBBT, Ledeberg-Ghent, Belgium 9050;ELIS Department, Multimedia Lab, Ghent University --- IBBT, Ledeberg-Ghent, Belgium 9050;Warringtonfiregent (WFRGent NV), Ghent, Belgium 9000;Department of Flow, Heat and Combustion Mechanics, Ghent University, Ghent, Belgium 9000;ELIS Department, Multimedia Lab, Ghent University --- IBBT, Ledeberg-Ghent, Belgium 9050

  • Venue:
  • Multimedia Tools and Applications
  • Year:
  • 2014

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Abstract

This paper proposes two novel time-of-flight based fire detection methods for indoor and outdoor fire detection. The indoor detector is based on the depth and amplitude image of a time-of-flight camera. Using this multi-modal information, flames can be detected very accurately by fast changing depth and amplitude disorder detection. In order to detect the fast changing depth, depth differences between consecutive frames are accumulated over time. Regions which have multiple pixels with a high accumulated depth difference are labeled as candidate flame regions. Simultaneously, the amplitude disorder is also investigated. Regions with high accumulative amplitude differences and high values in all detail images of the amplitude image its discrete wavelet transform, are also labeled as candidate flame regions. Finally, if one of the depth and amplitude candidate flame regions overlap, fire alarm is given. The outdoor detector, on the other hand, only differs from the indoor detector in one of its multi-modal inputs. As depth maps are unreliable in outdoor environments, the outdoor detector uses a visual flame detector instead of the fast changing depth detection. Experiments show that the proposed detectors have an average flame detection rate of 94% with no false positive detections.