Fire Detection Algorithms Using Multimodal ... - Bilkent University
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Fire Detection Algorithms Using Multimodal ... - Bilkent University

Fire Detection Algorithms Using Multimodal ... - Bilkent University Fire Detection Algorithms Using Multimodal ... - Bilkent University

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11.07.2015 Views

CHAPTER 3. FLAME DETECTION IN INFRA-RED (IR) VIDEO 49the movements of various patterns like smoke plumes produce correlated temporalsegments of gray-level pixels, which they called as temporal signatures. Foreach pixel inside an envelope of possible smoke regions, they recovered its last dluminance values to form a point P = [x 0 , x 1 , ..., x d−1 ] in d-dimensional “embeddingspace”. Luminance values were quantized to 2 e levels. They utilized fractalindexing using a space-filling Z-curve concept whose fractal rank is defined as:∑e−1∑d−1z(P ) = 2 l+jd x j l(3.4)j=0 l=0where x j lis the j − th bit of x l for a point P . They defined an instantaneous velocityfor each point P using the linked-list obtained according to Z-curve fractalranks. After this step, they estimated a cumulative velocity histogram (CVH)for each possible smoke region by including the maximum velocity among themand made smoke decisions about the existence of smoke according to the standarddeviation, minimum average energy, and shape and smoothness of thesehistograms [36].Our aim is to detect flames in manufacturing and power plants, large auditoriums,and other large indoor environments. So, we modified the method in [36]similar to the approach presented in Sec. 2.2. For comparison purposes, we replacedour wavelet-based contour analysis step with the CVH based method andleave the rest of the algorithm as proposed. We formed two three-state Markovmodels for flame and non-flame bright moving regions. These models were trainedfor each possible flame region using wavelet coefficients of CVH standard deviationvalues. States of HMMs were defined as in Sec. 3.2.2.Comparative detection results for some of the test videos are presented in Table3.1. The second column lists the number of frames in which flames exist inthe viewing range of the camera. The third and fourth columns show the numberof frames in which flames were detected by the modified CVH method explainedin the above paragraph, and our method, respectively. Our method detectedflame boundaries that have irregular shapes both temporally and spatially. Bothmethods detected fire in video clips V3–V6 and V8–V12 which contain actualfires indoor and outdoor. In video clip V3, flames were behind a wall most of the

CHAPTER 3. FLAME DETECTION IN INFRA-RED (IR) VIDEO 50time. The distance between the camera and fire ranges between 5 to 50 metersin these video clips. Video clips V1, V2, and V7 do not contain any fire. Thereare flames and walking people in the remaining clips. Some flame frames aremissed by both methods but this is not an important problem at all, becausethe fire was detected in the next frame or the frame after the next one. Themethod using CVH detected fire in most of the frames in fire containing videos,as well. However, it yielded false alarms in clips V1, V7 and V9–V12, in whichthere were a group of people walking by a car and around fire place. Proposedmethod analyzes the contours of possible fire regions in wavelet domain. Thismakes it more robust to slight contour changes than the modified method whichbasically depends on the analysis of motion vectors of possible fire regions.Flames of various burning materials have similar yet different temporal andspatial characteristics. For example, oil flame has a peak flicker frequency around15 Hz whereas it is around 5 Hz for coal flame (cf. Fig. 2 in [5]). In fact, flamesof the same burning material under different weather/wind conditions have alsodifferent temporal and spatial characteristics. What is common among variousflame types is the wide-band random nature in them causing temporal and spatialflicker. Our method exploits this stochastic nature. We use wavelet based featuresignals obtained from flickering flames to train and test our models in order to getrid of the effects of specific conditions forming the flames. The wavelet domainfeature signals capture the condition-independent random nature of flames.To verify the performance of our method with respect to flames of differentmaterials, we set up the following experiment:1. We train the model with ‘paper’ fire and test it with both ‘paper’ and‘paper + alcohol’ fires.2. We train the model with ‘paper + alcohol’ fire and test it with both ‘paper’and ‘paper + alcohol’ fires.The results of this experiment for some of the test clips are presented inTable 3.2. The results show that the method has similar detection rates fordifferent fires when trained with different flame types.

CHAPTER 3. FLAME DETECTION IN INFRA-RED (IR) VIDEO 50time. The distance between the camera and fire ranges between 5 to 50 metersin these video clips. Video clips V1, V2, and V7 do not contain any fire. Thereare flames and walking people in the remaining clips. Some flame frames aremissed by both methods but this is not an important problem at all, becausethe fire was detected in the next frame or the frame after the next one. Themethod using CVH detected fire in most of the frames in fire containing videos,as well. However, it yielded false alarms in clips V1, V7 and V9–V12, in whichthere were a group of people walking by a car and around fire place. Proposedmethod analyzes the contours of possible fire regions in wavelet domain. Thismakes it more robust to slight contour changes than the modified method whichbasically depends on the analysis of motion vectors of possible fire regions.Flames of various burning materials have similar yet different temporal andspatial characteristics. For example, oil flame has a peak flicker frequency around15 Hz whereas it is around 5 Hz for coal flame (cf. Fig. 2 in [5]). In fact, flamesof the same burning material under different weather/wind conditions have alsodifferent temporal and spatial characteristics. What is common among variousflame types is the wide-band random nature in them causing temporal and spatialflicker. Our method exploits this stochastic nature. We use wavelet based featuresignals obtained from flickering flames to train and test our models in order to getrid of the effects of specific conditions forming the flames. The wavelet domainfeature signals capture the condition-independent random nature of flames.To verify the performance of our method with respect to flames of differentmaterials, we set up the following experiment:1. We train the model with ‘paper’ fire and test it with both ‘paper’ and‘paper + alcohol’ fires.2. We train the model with ‘paper + alcohol’ fire and test it with both ‘paper’and ‘paper + alcohol’ fires.The results of this experiment for some of the test clips are presented inTable 3.2. The results show that the method has similar detection rates fordifferent fires when trained with different flame types.

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