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MINISTRY OF NATIONAL DEFENC E MILITARY TECHNICAL ACADEMY HA DAI DUONG APPROACHES TO VISUAL FEATURE EXTRACTION AND FIRE DETECTION BASED ON DIGITAL IMAGES Majored: Mathematical foundations for Informatics Code: 62 46 01 10 ABSTRACT OF PHD THESIS OF MATHEMATICS HA NOI - 2014 THIS THESIS IS COMPLETED AT MILITARY TECHNICAL ACADEMY - MINISTRY OF NATIONAL DEFENCE Scientific Supervisor: Assoc. Prof. Dr. Dao Thanh Tinh Reviewer 1: Assoc. Prof. Dr. Nguyen Duc Nghia Reviewer 2: Assoc. Prof. Dr. Dang Van Duc Reviewer 3: Assoc. Prof. Dr. Nguyen Xuan Hoai The thesis was evaluated by the examination board of the academy by the decision number / , / / of the Rector of Military Technical Academy, meeting at Military Technical Academy on … /… /……… This thesis can be found at: - Library of Le Quy Don Technical University - National Library of Vietnam 1 ABSTRACT Automatic fire detection has been interested for a long time because fire causes large scale damage to humans and our properties. Until now, some kinds of automatic detection devices, such as smoke detectors, flame or radiation detectors, or gas detectors, were invented. Although these traditional fire detection devices have proven its usefulness, they have some limitations; they are generally limited to indoors and require a close proximity to the fire; most of them can not provide additional information about fire circumstance. Recently, a new approach to automatic fire detection based on computer vision has lager attractive from researchers; it offers some advantages over the traditional detectors and can be used as complement for existing systems. This technique can detect the fire from a distance in large open spaces, and give more useful information about fire circumstance such as size, location, growth rate of fire, and in particularly it is potential to alarm early. This research concentrated on early fire detection based on computer vision. Firstly, some techniques that have been used for in the literature of automatic fire detection are reviewed. Secondly, some of visual features of fire region for early fire detection are examined in detail, which include a model of fire-color pixel, a model of temporal change detection, a model of textural analysis and a model of flickering verification; and a novel model of spatial structure of fire region. Finally, three models of fire detection based on computer vision at the early state of fire are presented: a model of early fire detection in general use-case (EVFD), a model of early fire detection in weak-light environment (EVFD_WLE), and a model of early fire detection in general use-case using SVM (EVFD_SVM). CHAPTER 1. INTRODUCTION 1.1 Automated fire detection problems Automatic fire detection has been interested for a long time because fire causes large scale damage to humans and our properties. Heat or thermal detectors are the oldest type of automatic detection device, originating from the mid-19th century with several types still in production today. Since then, other kinds of automatic detection devices, such as smoke detectors, flame or radiation detectors, or gas detectors, were invented. Although these traditional fire detection 2 devices have proven its usefulness, they have some limitations. Despite the advances in traditional fire alarm technology over the last century, losses caused by fire, such as deaths, permanent injuries, properties and environment damages still increase. In order to decrease this, timely detection, early fire localization and detection of fire propagation are essential. The problem of fire detection based on computer vision was initialized in early 1990s by Healey G. et al., since then various approach to this issue have been proposed. However, vision-based fire detection is not a completely solved problem as in most computer vision problems. The visual features of flames and smoke of an uncontrolled fire depend on distance, illumination and burning materials. In addition, cameras are not color and/or spectral measurement devices, they have sensors with different algorithms for color and illumination balancing, and therefore they may produce different images and video for the same scene. So that most proposed methods in vision-based fire detection return good results in some conditions of use-case, and may give bad results in other conditions. In particularly, existing vision-based fire detection methods are not adequate attention to alarm early. 1.2 Research objective For all above reasons the author have studied the topic “Approaches to visual feature extraction and fire detection based on digital images” with the main interest in the problem of vision-based fire detection at the early state of fire. Main question and also be motivation for this research is can vision-based fire detection give a fire alarm as soon as possible at the early state of fire?. This thesis wants to find out the answers for that question in some different use- case such as general conditions, weak-light environment, camera is dynamic. The objectives of this research include the following issues: 1) Firstly, some techniques that have been used for fire detection based on computer vision are reviewed. 2) Secondly, some of visual features of fire region such as color, texture, temporal change, flicker and spatial structure are examined in detail so that reducing the computational complexity of algorithm. 3) Thirdly, some models of early fire detection based on computer vision are developed. The development of each model relies on the analysis of the use-case such as for buildings and office surveillance, for 3 warehouse with weak light environment, etc. It is also applying intelligent classification to make the models more suitable and accurate. 1.3 Contributions This thesis makes the following contributions: 1. Develop and propose some methods of visual features of fire region extraction. Develop four new methods of pixel or fire region segmentation, these include a method of fire-color pixel based on Bayes classification in RGB space, a method of temporal change detection, a method of textural analysis and a method of flickering verification; and propose a novel approach to spatial structure of fire region by using top and rings features. 2. Propose a model of vision-based fire detection for early fire detection in general use-case - EVFD. This model is a combination of temporal change analysis, pixel classification based on fire-color process, and the flickering verification. 3. Propose a model of vision-based fire detection for early fire detection in weak-light environment - EVFD_WLE. This proposal is a combination of pixel classification based on fire-color process and analysis of spatial structure of fire region; these processes will be done if the environmental light is weak. 4. Propose a model of vision-based fire detection for early fire detection in general use-case using SVM - EVFD_SVM. In this model, the algorithm consists of three main tasks: pixel-based processing using fire-color process for pixel classification, temporal change detection, and recover lack pixel; textural features of potential fire region extraction; and SVM classification for distinguishing a potential fire region as fire or non-fire object. 1.4 Thesis outline This thesis is organized as follows: Chapter 1, Introduction, presents the need of problem of fire detection based on computer vision, disadvantages of traditional fire detection systems, and advantages of fire detection based on computer vision. This chapter also describes problem of research, research question, main contributions and structure of the thesis. Chapter 2, Fire detection techniques based on computer vision: A review, review some techniques that have been used for fire detection based on computer 4 vision. Chapter 3, Visual feature extraction for fire detection, presents examining in detail some of visual features of fire region for early fire detection; and then develops four new models of pixel or fire region segmentation and proposes a novel model of spatial structure of fire region. Chapter 4, Early fire detection based on computer vision, presents three models of fire detection based on computer vision: early fire detection in general use-case, early fire detection in weak-light environment, and early fire detection in general use-case using SVM. Chapter 5, Conclusions and Discussions, states the conclusions, presents the contributions and summarizes the results obtained throughout the thesis and recommendations future research of problem. CHAPTER 2. FIRE DETECTION BASED ON COMPUTER VISION: A REVIEW 2.1 Introduction Automatic fire detection has been interested for a long time due to its large scale damage to humans and our properties. Heat or thermal detectors are the oldest type of automatic detection device originating from the mid-19th century. Since then, other kind of automatic detection devices; smoke detectors, flame or radiation detectors, or gas detectors for examples have been being developed. Although these devices have proven its usefulness in some conditions, they have some limitations. They are generally limited to indoors and require a close proximity to the fire. Most of them can not provide additional information about fire circumstances and may take a long time to raise alarm. Fire detection based on computer vision can be marked by the research of Healey G. et al. in the early 1990s. Since then, various approaches to this issue were proposed. The general scheme of fire detection based on computer vision is a combination of two components: the analysis of visual features and the classification techniques. The visual features include color, temporal changes, spatial variance, texture and flickering. The classification techniques are used to classify a pixel as fire or as non-fire, or to distinguish a potential fire region as fire or as non-fire object; these techniques include Gaussian Mixture Model (GMM), Bayes classification, Support Vector Machine (SVM), Markov Model and Neural Network, etc. 5 2.2 Visual features analysis 2.2.1 The chromatic color Color detection is one of the most important and earlier feature used in vision-based fire detection. The majority of the color-based approaches in this trend make use of RGB color space, sometimes in combination with HSI/HSV color space. Some fire-color models often use in the literature of vision-based fire detection such as statistical generated color models, Gaussian Mixture Models (GMM). Based on the analysis of color of flame in red-yellow rang, a common type of flame in the real-word, a fire-color model to segment a pixel is proposed as follows: 1 : C R R R  , 2 : C R R G and G B   ,   3 : (255 )* C S R R S R    , and the fire-color model is defined as: 1 2 3 1 ( ) ( ) ( ) ( , ) 0 C if R and R and R Fire x y Otherwise     where R, G, B are red, green and blue components of pixel at (x,y) respectively; S is the saturation component in HSI color space; S T and R T are two experimental factors. Several other works detect fire- color pixel using more complex model such as Gaussian mixture model. In this model, with a given pixel, if its color value is inside one of distribution then it is considered as a fire-color pixel. Denote d(r 1 , g 1 , b 1 , r 2 , g 2 , b 2 ) is the measurement distance from (r 1 , g 1 , b 1 ) to (r 2 , g 2 , b 2 ) in 3-dimensional RGB space. The fire-color model based on GMM is described as 1 { : ( , , , , , ) 2 } ( , ) , 1 10 0 i i i i Tr if i d R G B R G B v Fire x y Otherwise i         in which R i , G i , B i are the mean of red, green blue components of Gaussian distribution i-th; v i is its standard deviation. 2.2.2 The temporal changes Color model alone is not enough to identify fire pixel or fire region. There are many objects that share the same color as fire. An important visual feature to distinguish between fire and fire-like objects is the temporal change of fire. To analyze temporal changes, it may cause by flame, almost proposals assume that the camera is 6 stationary. A simple approach to estimate the background is to average the observed image frames of the video. Let I(x,y,n) represent the intensity value of the pixel at location (x,y) in the n-th frame, I, background intensity value, B(x,y,n+1) at the same pixel position is calculated as follows: ( , , ) (1 ) ( , , ) if ( , ) is stationary ( , , 1) ( , , ) if ( , )is moving aB x y n a I x y n x y B x y n B x y n x y        where B(x,y,n) is the previous estimate of the background intensity value at the same pixel position. The update parameter a is a positive real number close to one. Initially, B(x,y,0) is set to the first frame, I(x,y,0). The pixel at (x,y) is assumed to be moving if ( , , ) ( , , 1) ( , , )I x y n I x y n T x y n    where I(x,y,n-1) is the intensity value of the pixel at (x,y) in the (n-1)-th frame, T(x,y,n) is a recursively updated threshold at (x,y) of frame n. Other method usually used to analysis temporal changes is frames difference. 2.2.3 The textural and spatial difference Flames of an uncontrolled fire have varying colors even within a small area since spatial color difference analysis focuses on this characteristic. Using range filters, variance/histogram analysis, or spatial wavelet analysis, the spatial color variations in pixel values is analyzed to distinguish between fire and fire-like object. Using wavelet analysis, Toreyin et al. compute a value, v, to estimate spatial variations as follows: 2 2 2 , 1 ( , ) ( , ) ( , ) lh hl hh x y v s x y s x y s x y M N      where s lh (x,y) is the low-high sub-image, s hl (x,y) is the high-low sub- image, and s hh (x,y) is the high-high sub-image of the wavelet transform, respectively, and MN is the number of pixels potential fire region. If the decision parameter, v, exceeds a threshold, then it is likely that this region under investigation is a fire region. In other way, Borges et al. use a well-known metric, the variance, to indicate the amount of coarseness in the pixel values. For a potential fire region, R, the variance of pixels is computed as 2 ( , ) ( , ) ) ( ( , ) x y R c I x y I p I x y     in which I(x,y) is intensity of pixel at (x,y), p() is the normalized 7 histogram, and I is the mean intensity in R. Therefore, fire is assumed if the region is with a variance c > λσ, where λσ is determined from a set of experimental analysis. 2.3 Classification techniques Some popular approaches to the classification of the multi- dimensional feature vectors obtained from each candidate flame region are Bayes classification and SVM classification. Other classification methods that have been used in the literature of vision- based fire detection include neural networks, Markov models, etc. This section introduces two classification methods that used in the research: Bayes and SVM classification. 2.4 Conclusion The development of application based on computer vision for fire detection, which can raise alarm quickly and accurately, is essential. However, vision-based fire detection is not a completely solved problem as in most computer vision problems. The visual features of flames of an uncontrolled fire depend on the distance, illumination and burning materials. In addition, cameras are not color and/or spectral measurement devices, they have sensors with different algorithms for color and illumination balancing, and therefore they may produce different images and video for the same scene. For the above reasons, the research of vision-based fire detection is necessary. In general, most proposed methods in vision-based fire detection returns good results in some conditions of use-case, and may give bad results in other conditions. In particularly, current vision-based fire detection methods are not adequate attention to alarm early so that research of vision-based fire detection is necessary, and using this technique for early fire detection is an important issue. CHAPTER 3. VISUAL FEATURE EXTRACTION FOR FIRE DETECTION This chapter presents the examining in detail some of visual features of fire region for early fire detection; and then develop four new models of pixel or fire region segmentation, these include a model of fire-color pixel, a model of temporal change detection, a model of textural analysis and a model of flickering verification; and propose a novel model of spatial structure of fire region. 8 3.1 A new approach to color extraction 3.1.1 Chromatic analysis The model of fire-color is usually used in the first step of the process and is crucial to the final result. The general idea of most proposals in the VFD literature is to determine the fire-color model, Fire(x, y) for pixel at (x,y), and then using that model to build the potential fire mask, PFM(x,y), as follows: 1 if ( , ) ( , ) 0 Otherwise Fire x y PFM x y     After that the mask PFM is used to analyze the other characteristics of fire such as temporal changes, deformation of the boundary, surface statistical parameters, etc. The main drawback of existing model for fire-color detection is fixed; it returns good results in some situations and raise bad results in some others. For more flexible, this study proposes a color model of pixel in fire region using Bayesian classification; rely on the red (R), green (G), and blue (B) components a model of fire-color to classify a pixel into two classes, fire or non-fire pixel is developed. 3.1.2 Classification based on Bayes For pixel p at (x,y), a vector v = [R, G, B] T is considered in terms of sample for classification problem; in which R, G, and B are red, green and blue component of p. Let g 1 (v) and g 2 (v) are two discriminatory functions based on Bayesian classification for fire and non-fire classes of pixel p; if g 1 (v)>g 2 (v) then p belong to fire class, otherwise p belong to non-fire class. Denote  1 is set of fire class samples,  2 is set of non-fire class samples, Bayessian discriminatory functions are defined as follows: 1 1 1 1 ( ) T T vg v v W w cv    2 2 2 2 ( ) T T vg v v W w cv    in which 1 1 1 1 2 W C    , 2 2 1 1 2 W C    , 1 1 1 1 w C m   , 2 2 2 1 w C m   , 1 1 1 1 1 1 1 1 1 ln | | ( ) 2 2 T c C mm C P       , 2 2 2 2 2 1 2 1 1 ln | | ( ) 2 2 T c C mm C P      where m 1 is mean and C 1 is covariance matrix of  1 and m 2 is mean and C 2 is covariance matrix of  2 . Then fire-color model, denote [...]... total 1200 frames are used For comparison, frames difference model, background subtraction model, and proposed model are 10 implemented The evaluation of time performance is shown in Table 1, and the quality of temporal change detection is shown in Figure 3 Figure 2 The scheme of partition of two frames for temporal analysis Method Time performance per frame (Milliseconds) Frames difference 23.7 Background... detection Figure 4 shows the ROC (Receiver Operating Characteristic) curve of temporal changes detection for threshold T Rely on this evaluation, when the threshold T = 0.025 then true positive fraction equal to 95% and false positive fraction is 6% 11 3.2.2 Textural analysis Intuitively, fire has unique visual signatures such as color and texture The textural features of a fire region includes average . which 1 1 1 1 2 W C    , 2 2 1 1 2 W C    , 1 1 1 1 w C m   , 2 2 2 1 w C m   , 1 1 1 1 1 1 1 1 1 ln | | ( ) 2 2 T c C mm C P       , 2 2 2 2 2 1 2 1 1 ln | | ( ) 2 2 T c C mm. 68e-3*R*R+.11e -2* R*G 44e-3*R*B 82e-3*G*G +.98e-3*G*B 47e-3*B*B+.16*R 92e-1*G+.33e-1*B -23 . g 2 = 73e-3*R*R+ .26 e -2* R*G 13e -2* R*B 39e -2* G*G +.52e -2* G*B 20 e -2* B*B+.30e-1*R 98e -2* G+.56e -2* B- 12 The results. samples of group 2: g 1 = 42e-3*R*R+.13e -2* R*G 56e-3*R*B 14e -2* G*G+.12e -2* G*B 41e-3*B*B+.46e-1*R 44e-1*G+.63e-1*B-17. g 2 = 98e-3*R*R+ .26 e -2* R*G 10e -2* R*B 35e -2* G*G +.46e -2* G*B 20 e -2* B*B+.37e-1*R