An image processing technique for fire detection in video images

Marbach, Giuseppe; Loepfe, M.; Brupbacher, Thomas

doi:10.1016/j.firesaf.2006.02.001

Cited by 228 publications

(105 citation statements)

References 2 publications

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“…Marbach et al [7] propose a model where data are represented and analysed in a YUV space. Here the features used to detect fire are the time derivative of the luminance component Y for a first re- duction of the candidate fire pixel; then the crominance components U and V are used for classification.…”

Section: State Of the Artmentioning

confidence: 99%

Early fire and smoke detection based on colour features and motion analysis

Morerio

Regazzoni

Gera³

2012

2012 19th IEEE International Conference on Image Processing

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This work addresses the issue of fire and smoke detection in a scene within a video surveillance framework. Detection of fire and smoke pixels is at first achieved by means of a motion detection algorithm. In addition, separation of smoke and fire pixels using colour information (within appropriate spaces, specifically chosen in order to enhance specific chromatic features) is performed. In parallel, a pixel selection based on the dynamics of the area is carried out in order to reduce false detection. The output of the three parallel algorithms are eventually fused by means of a MLP.

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Section: State Of the Artmentioning

confidence: 99%

Early fire and smoke detection based on colour features and motion analysis

Morerio

Regazzoni

Gera³

2012

2012 19th IEEE International Conference on Image Processing

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“…Other systems are also exploited. Among these systems are those permitting the extraction of luminance-chrominance components, such as YCbCr [4,41,34],YUV [20] and L * a * b * [3]. There are also work using so-called cylindrical systems such as TSI [15] and TSV [18].…”

Section: Introductionmentioning

confidence: 99%

Computer vision for wildfire research: An evolving image dataset for processing and analysis

Toulouse

Rossi

Campana

et al. 2017

Fire Safety Journal

128

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“…Celik et al [4], make use of fire detection rules after extracting foreground pixels by means of a change detection algorithm. They used method for the classification of fire/smoke pixels, separating them from the background.. Marbach et al [5] propose a model where data are represented and analyzed in a YUV space. This model is used for representing the video data, where time derivation of luminance component Y used for first reduction of the candidate fire pixel, and chrominance component U and V used for classification whether or not the candidate pixels were in the fire section.…”

Section: Introductionmentioning

confidence: 99%

Flame Detection using Image Processing Techniques

Patel¹,

Tiwari²

2012

IJCA

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Dynamic textures are common in natural scenes. Examples of dynamic textures in video include fire, smoke, trees in the wind, clouds, sky, ocean waves etc. The fire is characterized using efficient features and detection of the same using a suitable processing. Every pixel is checked for the presence or absence of fire using color features, and periodic behavior in fire regions is also analyzed. In this paper we use combined approach of color detection, motion detection and area dispersion to detect fire in video data. Firstly, the algorithm locates desired color regions in video frames, and then determines the region in the video where there is any movement, and in the last step we calculate the pixel area of the frame. The combination of color, motion and area clues is used to detect fire in the video.

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An image processing technique for fire detection in video images

Cited by 228 publications

References 2 publications

Early fire and smoke detection based on colour features and motion analysis

Early fire and smoke detection based on colour features and motion analysis

Computer vision for wildfire research: An evolving image dataset for processing and analysis

Flame Detection using Image Processing Techniques

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