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QuickBlaze: Early Fire Detection Using a Combined Video Processing Approach

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Abstract

Optical fire sensors, sometimes called “volumetric” sensors, are complementary to conventional point sensors such as smoke and heat detectors in providing people with early warnings of fire incidents. Cameras combined with image processing software hold the promise of detecting fire incidents more quickly than point sensors and can also provide size, growth, and direction information more readily than their conventional counterparts. In this paper, we present QuickBlaze, a flame and smoke detection system based on vision sensors aimed at early detection of fire incidents for open or closed indoor and outdoor environments. We use simple image and video processing techniques to compute motion and color cues, enabling segmentation of flame and smoke candidates from the background in real time. We begin with color balancing, then separate smoke and flame detection streams operate on the image. Both streams identify candidate regions based on color information then perform morphological image processing on the candidates. The smoke detection stream then filters candidate regions based on turbulence flow rate analysis, and the flame detection stream filters based on growth and flow rate information. QuickBlaze does not require any offline training, although manual adjustment of parameters during a calibration phase is required to cater to the particular camera’s depth of view and the surrounding environment. In an extensive empirical evaluation benchmarking QuickBlaze against commercial fire detection software, we find that it has a better response time, is 2.66 times faster, and better localizes fire incidents. Detection of fire using our real-time video processing approach early on in the burning process holds the potential to decrease the length of the critical period from combustion to human response in the event of a fire.

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Acknowledgments

WSQ was supported by fellowships from the faculty development program at the National University of Science and Technology (NUST), Pakistan, and the Asian Institute of Technology. We thank Prof. A. Enis Çetin for the demo license for VisiFire software.

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Authors and Affiliations

  1. Asian Institute of Technology, Klong Luang, Pathumthani, 12120, Thailand

    Waqar S. Qureshi, Mongkol Ekpanyapong, Matthew N. Dailey & Suchet Rinsurongkawong

  2. Tula State University Cybernetics Faculty, Tula, Russia

    Anton Malenichev & Olga Krasotkina

Authors
  1. Waqar S. Qureshi
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  2. Mongkol Ekpanyapong
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  3. Matthew N. Dailey
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  4. Suchet Rinsurongkawong
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  5. Anton Malenichev
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  6. Olga Krasotkina
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Correspondence to Mongkol Ekpanyapong.

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Qureshi, W.S., Ekpanyapong, M., Dailey, M.N. et al. QuickBlaze: Early Fire Detection Using a Combined Video Processing Approach. Fire Technol 52, 1293–1317 (2016). https://doi.org/10.1007/s10694-015-0489-7

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