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Multi-modal time-of-flight based fire detection

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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.

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Acknowledgements

The research activities as described in this paper were funded by Ghent University, the Interdisciplinary Institute for Broadband Technology (IBBT), University College West Flanders (HOWEST), Warringtonfiregent (WFRGent NV), the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT), the Fund for Scientific Research-Flanders, the Belgian Federal Science Policy Office (BFSPO) and the European Union.

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Authors and Affiliations

  1. ELIS Department, Multimedia Lab, Ghent University – IBBT, Gaston Crommenlaan 8, bus 201, 9050, Ledeberg-Ghent, Belgium

    Steven Verstockt, Pieterjan De Potter, Peter Lambert, Charles Hollemeersch & Rik Van de Walle

  2. ELIT Lab, University College West Flanders, Ghent University Association, Graaf Karel de Goedelaan 5, 8500, Kortrijk, Belgium

    Steven Verstockt & Sofie Van Hoecke

  3. Warringtonfiregent (WFRGent NV), Ottergemsesteenweg 711, 9000, Ghent, Belgium

    Bart Sette

  4. Department of Flow, Heat and Combustion Mechanics, Ghent University, Sint-Pietersnieuwstraat 41, 9000, Ghent, Belgium

    Bart Merci

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  1. Steven Verstockt
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Correspondence to Steven Verstockt.

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Verstockt, S., Van Hoecke, S., De Potter, P. et al. Multi-modal time-of-flight based fire detection. Multimed Tools Appl 69, 313–338 (2014). https://doi.org/10.1007/s11042-012-0991-6

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