Application of neural network to analyses of CCD colour TV-camera image for the detection of car fires in expressway tunnels Speaker: Wu Wei-Cheng Date: 2009/06/15 1

Outline 1. Introduction 2. Simulation fire experiment 3. Flow of processing 4. Extraction of flame image 5. Extraction of the feature parameters 6. Fire detection by NN 7. Conclusions 2

1. Introduction   The detection of a car fire using a neural network (NN), which uses features of flame images in a simulation fire as input elements.   The simulation fire is photographed with a CCD colour TV camera.   Flame images are taken from the dynamic image, and features of the images for the NN application are extracted from a rectified image 3

 The simulation fire experiment of a car fire in a tunnel considered the following situations: 1.A burning car has stopped. 2.A burning car is moving. 3.A following car or an oncoming car is approaching the burning car in situation Simulation fire experiment

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6 3. Flow of processing

 The one-frame image is compared with the background image on Red intensity by calculating its difference Extraction of flame image

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10 4. Extraction of flame image

 Before finding feature parameters, it is necessary to standardise the image.  The standardisation is to expand or reduce the zone estimated to be flame into a size based on a standardisation distance.  The standardisation distance is set to 60m and the process is performed with linear interpolation Extraction of the feature parameters

12 5. Extraction of the feature parameters

 The histogram and quartile (Q(0.25), Q(0.5), Q(0.75)), and quantile (Q(0.1), Q(0.9)) of Green intensity of the flame Extraction of the feature parameters

 The input element to NN regarding colour information is the value which normalised the quartile and quantile of Red, Green, and Blue intensity.  The input element to NN regarding the area of flame is the normalised area Extraction of the feature parameters

 If a fire output is 0.7 or more and a non-fire output is 0.3 or less, it is defined as the fire and its opposite is defined as a non-fire Fire detection by NN

 The error reverse spreading method is used by the NN to determine the following: A flame which has changed brightness with the iris diaphragm of the camera and a ND filter. A flame which has changed brightness with the iris diaphragm of the camera and a ND filter. A brake lamp. A brake lamp. A headlight. A headlight. Road surface reflection of a headlight. Road surface reflection of a headlight. A revolving emergency light. A revolving emergency light Fire detection by NN

17 6. Fire detection by NN

18 6. Fire detection by NN

7. Conclusions  A car fire which occurs less than 150m from a surveillance camera is detectable by standardising the distance.  A car fire with flames 50 cm high is clearly detectable using the NN which uses colour and area information as the input elements. 19