Video Fire Detection – Techniques and Applications in the Fire Industry

Xiong, Ziyou; Caballero, Rodrigo E.; Wang, Hongcheng; Finn, Alan; Peng, Pei-Yuan

doi:10.1007/978-0-387-76569-3_12

Cited by 12 publications

(12 citation statements)

References 7 publications

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“…The fuzzy logic reasoning, in this case, is the counterpart of our probabilistic approach proposed in Section III. Fuzzy logic is also used in [21] to fuse features like average intensity, average hue and average flickering. In both [20] and [21], however, the assumptions and reported experiments consider a static camera, which is often not the case in newscast videos.…”

Section: Related Methodsmentioning

confidence: 99%

A Probabilistic Approach for Vision-Based Fire Detection in Videos

Borges¹,

Izquierdo

2010

IEEE Trans. Circuits Syst. Video Technol.

203

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Abstract-Automated fire detection is an active research topic in computer vision. In this paper, we propose and analyze a new method for identifying fire in videos. Computer vision-based fire detection algorithms are usually applied in closed-circuit television surveillance scenarios with controlled background. In contrast, the proposed method can be applied not only to surveillance but also to automatic video classification for retrieval of fire catastrophes in databases of newscast content. In the latter case, there are large variations in fire and background characteristics depending on the video instance. The proposed method analyzes the frame-to-frame changes of specific low-level features describing potential fire regions. These features are color, area size, surface coarseness, boundary roughness, and skewness within estimated fire regions. Because of flickering and random characteristics of fire, these features are powerful discriminants. The behavioral change of each one of these features is evaluated, and the results are then combined according to the Bayes classifier for robust fire recognition. In addition, a priori knowledge of fire events captured in videos is used to significantly improve the classification results. For edited newscast videos, the fire region is usually located in the center of the frames. This fact is used to model the probability of occurrence of fire as a function of the position. Experiments illustrated the applicability of the method.

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Section: Related Methodsmentioning

confidence: 99%

A Probabilistic Approach for Vision-Based Fire Detection in Videos

Borges¹,

Izquierdo

2010

IEEE Trans. Circuits Syst. Video Technol.

203

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“…The temporal periodicity can be calculated using Fast Fourier Transform (FFT), Wavelet Transform or Mean Crossing Rate (MCR). In our system, we have used the Mean Crossing Rate (MCR) method [3].…”

Section: Flickering Extractionmentioning

confidence: 99%

Motion Blobs as a Feature for Detection on Smoke

A¹,

Pande²

2011

SpecialIssue

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Abstract-Disturbance that is caused due to visual perception with the atmosphere is coined as smoke, but the major problem is to quantify the detected smoke that is made up of small particles of carbonaceous matter in the air, resulting mainly from the burning of organic material. The present work focuses on the detection of smoke immaterial it being accidental, arson or created one and raise an alarm through an electrical device that senses the presence of visible or invisible particles or in simple terms a smoke detector issuing a signal to fire alarm system / issue a local audible alarm from detector itself.

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“…Conventional point-based smoke sensors detect the presence of smoke particles by ionization or photometry [7]. Since smoke particles must be transported from the fire to the detector, such system fails to give an early fire warning in the outdoors and open areas; hence, it cannot be used to detect forest fires.…”

Section: Introductionmentioning

confidence: 99%

“…interfering objects, and generally because smoke is difficult to model, and most of the common image processing methods do not characterize smoke well [7].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, Dynamic smoke characteristics consist of features that change over time, such as the shape, expansion, motion, orientation, and/or variations in its color. Other frequently used fire detection techniques include energy analysis [27] [28] [29], and flicker detection [7] [14] [30] [31] [32]. Both approaches also rely on the temporal behavior of smoke and flames.…”

Section: Introductionmentioning

confidence: 99%

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Early Forest Fire Detection Using Texture, Blob Threshold, and Motion Analysis of Principal Components

Mr.¹,

Fouad²

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Forest fires constantly threaten ecological systems, infrastructure and human lives. The purpose behind this study is minimizing the devastating damage caused by forest fires. Since it is impossible to completely avoid their occurrences, it is essential to accomplish a fast and appropriate intervention to minimize their destructive consequences. The most traditional method for detecting forest fires is human based surveillance through lookout towers. However, this study presents a more modern technique. It utilizes land-based real-time multispectral video processing to identify and determine the possibility of fire occurring within the camera's field of view. The temporal, spectral, and spatial signatures of the fire are exploited. The methods discussed include: (1) Range filtering followed by entropy filtering of the infrared (IR) video data, and (2) Principal Component Analysis of visible spectrum video data followed by motion analysis and adaptive intensity threshold. The two schemes presented are tailored to detect the fire core, and the smoke plume, respectively. Cooled Midwave Infrared (IR) camera is used to capture the heat distribution within the field of view. The fire core is then isolated using texture analysis techniques: first, range filtering applied on two consecutive IR frames, and then followed by entropy filtering of their absolute difference. Since smoke represents the earliest sign of fire, this study also explores multiple techniques for detecting smoke plumes in a given scene. The spatial and temporal variance of smoke plume is captured using temporal Principal Component Analysis, PCA. The results show that a smoke plume is readily segmented via PCA applied on the visible Blue band over 2 seconds sampled every 0.2 v seconds. The smoke plume exists in the 2 nd principal component, and is finally identified, segmented, and isolated, using either motion analysis or adaptive intensity threshold. Experimental results, obtained in this study, show that the proposed system can detect smoke effectively at a distance of approximately 832 meters with a low false-alarm rate and short reaction time. Applied, such system would achieve early forest fire detection minimizing fire damage.

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Video Fire Detection – Techniques and Applications in the Fire Industry

Cited by 12 publications

References 7 publications

A Probabilistic Approach for Vision-Based Fire Detection in Videos

A Probabilistic Approach for Vision-Based Fire Detection in Videos

Motion Blobs as a Feature for Detection on Smoke

Early Forest Fire Detection Using Texture, Blob Threshold, and Motion Analysis of Principal Components

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