Flame and smoke detection method for early real-time detection of a tunnel fire

Han, Dongil; Lee, Byoungmoo

doi:10.1016/j.firesaf.2009.05.007

Cited by 81 publications

(40 citation statements)

References 9 publications

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“…It not only reduces the computational complexity but also mitigates the interference due to noise and distractors. Widely used methods include background subtraction [9], [16], the motion history image [17] and the adaptive background estimation based on GMM [18].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Flame Detection in Videos With a Dirichlet Process Gaussian Mixture Color Model

Mihaylova

Isupova

et al. 2018

IEEE Trans. Ind. Inf.

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Abstract-This paper proposes a flame detection framework based on the color, dynamics and flickering properties of flames. The distribution of flame colors is modelled by a Gaussian Mixture Model whose number of Gaussian component is estimated by a Dirichlet process from training data rather than set empirically. The proposed approach estimates the flame color distribution more accurately as it can determine the number of Gaussian components of the mixture model automatically. Additionally, a probabilistic saliency analysis method and a one-dimensional wavelet transform are used to extract motion saliency and filtered temporal series as features, describing the dynamics and flickering properties of flames. The developed Dirichlet Process Gaussian Mixture Model based approach for autonomous flame detection is tested on various videos and achieves frame-wise accuracy higher than 95%.

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Section: Introductionmentioning

confidence: 99%

Autonomous Flame Detection in Videos With a Dirichlet Process Gaussian Mixture Color Model

Mihaylova

Isupova

et al. 2018

IEEE Trans. Ind. Inf.

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“…In previous studies, [6][7][8][9][10] information from each pixel in the image was used to determine whether fires exist in the images. Alternatively, our paper proposes a method to model the local characteristics of fire, represented in sequential images based on patches of an input image, and to detect a fire area by using a supervised learning algorithm.…”

Section: Fire Detection Algorithmmentioning

confidence: 99%

“…For example, red clothes, moving cars, and lights will be recognized as fires because only color information was used. Han 7 used color and motion information to present real-time fire detection. Töreyin et al 8 used temporal and spatial wavelet analysis to separate the fire regions from sequential images.…”

Section: Introductionmentioning

confidence: 99%

Fire detection system using random forest classification for image sequences of complex background

Kim¹,

Kang²

2013

Opt. Eng

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Abstract. We present a fire alarm system based on image processing that detects fire accidents in various environments. To reduce false alarms that frequently appeared in earlier systems, we combined image features including color, motion, and blinking information. We specifically define the color conditions of fires in hue, saturation and value, and RGB color space. Fire features are represented as intensity variation, color mean and variance, motion, and image differences. Moreover, blinking fire features are modeled by using crossing patches. We propose an algorithm that classifies patches into fire or nonfire areas by using random forest supervised learning. We design an embedded surveillance device made with acrylonitrile butadiene styrene housing for stable fire detection in outdoor environments. The experimental results show that our algorithm works robustly in complex environments and is able to detect fires in real time.

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“…Maintenance and management of these tunnels are now important, due to increased demand for greater use and the needs for construction of tunnels [1]. Therefore, because of increased usability of tunnels, the prevention of fire and emergency evacuation becomes important matters to consider.…”

Section: Introductionmentioning

confidence: 99%

CFD Calculations of Soot Radiation Absorption Coefficients during Pool Tunnel Fire

Davidy¹

2017

IOSR JMCE

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Flame and smoke detection method for early real-time detection of a tunnel fire

Cited by 81 publications

References 9 publications

Autonomous Flame Detection in Videos With a Dirichlet Process Gaussian Mixture Color Model

Autonomous Flame Detection in Videos With a Dirichlet Process Gaussian Mixture Color Model

Fire detection system using random forest classification for image sequences of complex background

CFD Calculations of Soot Radiation Absorption Coefficients during Pool Tunnel Fire

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