Video fire detection - Review

A. Enis Çetin; Kosmas Dimitropoulos; Benedict Gouverneur; Nikos Grammalidis; Osman Günay; Y. Hakan Habiboǧlu; B. Uǧur Töreyin; Steven Verstockt

doi:10.1016/j.dsp.2013.07.003

Video fire detection - Review

A. Enis Çetin, Kosmas Dimitropoulos, Benedict Gouverneur, Nikos Grammalidis, Osman Günay, Y. Hakan Habiboǧlu, B. Uǧur Töreyin^*, Steven Verstockt

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

262 Citations (Scopus)

Abstract

This is a review article describing the recent developments in Video based Fire Detection (VFD). Video surveillance cameras and computer vision methods are widely used in many security applications. It is also possible to use security cameras and special purpose infrared surveillance cameras for fire detection. This requires intelligent video processing techniques for detection and analysis of uncontrolled fire behavior. VFD may help reduce the detection time compared to the currently available sensors in both indoors and outdoors because cameras can monitor "volumes" and do not have transport delay that the traditional "point" sensors suffer from. It is possible to cover an area of 100 km² using a single pan-tilt-zoom camera placed on a hilltop for wildfire detection. Another benefit of the VFD systems is that they can provide crucial information about the size and growth of the fire, direction of smoke propagation.

Original language	English
Pages (from-to)	1827-1843
Number of pages	17
Journal	Digital Signal Processing: A Review Journal
Volume	23
Issue number	6
DOIs	https://doi.org/10.1016/j.dsp.2013.07.003
Publication status	Published - Dec 2013
Externally published	Yes

Funding

This work was supported in part by FIRESENSE (Fire Detection and Management through a Multi-Sensor Network for the Protection of Cultural Heritage Areas from the Risk of Fire and Extreme Weather Conditions, FP7-ENV-2009-1244088-FIRESENSE ) and by The Scientific and Technological Research Council of Turkey (TUBITAK) under grant EEEAG 111E217 .

Funders	Funder number
TUBITAK	EEEAG 111E217
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu

Keywords

Computer vision
Covariance matrices
Decision fusion
Smoke detection
Video based fire detection
Wavelets

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10.1016/j.dsp.2013.07.003

Cite this

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AU - Grammalidis, Nikos

AU - Günay, Osman

AU - Habiboǧlu, Y. Hakan

AU - Töreyin, B. Uǧur

AU - Verstockt, Steven

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KW - Decision fusion

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IS - 6

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Video fire detection - Review

Abstract

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Keywords

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