Image Features As a Feed To Rough Sets for Fire/Smoke Detection

Transcription

1 101 Image Feature A a Feed To Rough Set for Fire/Smoke Detection Jean Paul Dukuzumuremyi 1, Beiji Zou 2 and Damien Hanyurwimfura 3 1,2 School of Information Science and Engineering, Central South Univerity Changha, Hunan, China 3 College of Information Science and Engineering Changha, Hunan, China Abtract Many reearcher are being involved in fire detection uing camera a information data. However earlier reearche have not paid attention to the determination of the type of fire (naked or moldering). Thi paper propoe an improved fire detection method baed on the image feature that are taken a a feed to a rough et information ytem (RSIS) in order to not only determine the type of the fire but alo to minimize the fale alarm ignal from the method baed on phyical apect of fire (temperature, moke enor, etc.) and other frequency baed image feature WMF(Wavelet-baed model) and FDS(Fire detection baed on dicrete coine coefficient) propoed earlier. Keyword: Image proceing, fire detection, computer viion, information ytem. 1. Introduction Fire hazard are becoming one of the mot important fear of mankind. Many reearcher are now tudying a deciionmaking method for fire detection data fuion baed on rough et approach by introducing an attribute reduction method baed on the characteritic of conitent approximation pace (CAS) of rough et a tated in[3]. Thi paper conider an image information data and contribute to the choice of image data attribute to imulate that conitent approximation pace. A nowaday alarming on the indoor fire i relatively being mature, fire detector have been divided into different categorie uch u: SSD-Smoke Sening Detector, TSD-Temperature ening detector and LSD-Light Sening Detector [3]. All thee type of fire have different level of detection to tract out the type and the tage of fire. Due to pace contraint the type enumerated above can have difficultie in timely detecting fire that are very ditanced from the enor. Therefore, uing camera to detect fire can greatly improve the ituation of producing fale alarming ignal and increae the afety by granting a quicker repone. 2. Related work Many fire detection ytem that ue conventional moke detection ytem depend on point moke detector in order to tet the preence of ome particle generated by moke uing ionization or photometry. However, uch point detector inherently uffer from the tranport delay of the moke from the fire to the enor[12]. Many improvement that ue computer viion are baed on the color cue of fire, and the fact that color i very enitive to illumination change[10-11] prove that frequency domain feature could be more reliable cue than color in general ituation, the reaon why a new colour-baed model of fire appearance a well a a new wavelet-baed model of fire frequency ignature were propoed in [8] and another one baed on dicrete coine coefficient trained with neural network were propoed in [9]. Thi paper propoe an improvement by an attribute reduction method baed on the image feature taken a a feed to a rough et information data (RSFD), by uing image information fuion technique that would involve information proceing, pattern recognition, and deciion proce. The information can be fued at data layer, feature layer and deciion layer in the time domain, patial domain, or other domain, the mot important contribution i to even determine the type of fire (moldering or naked) o that the fire can be dealt with accordingly.

2 Image a a Rough Set Data Information. 3.1 The choice of Rough Set Fire type are varied in nature leading to an information redundancy and uncertainty. It i then neceary to find a tool that can deal with uch a challenge. Rough Set theory propoed by Pawlak i one of the firt non-tatitical approache for uch data analyi in one of it fundamental concept which i knowledge reduction for data claification. 3.2 Repreentation of Image a a rough et information Image data (feature) can be conidered a an information ytem, deciion ytem or inconitent deciion information ytem in order to be dealt with by the mean of rough et technique. If the attribute reduction method and attribute feature in the ample are defined, we can then get attribute reduction method and attribute feature in all other to be invetigated. The deciion rule can be obtained through the fuion of training data et. Definition 1: Let U={x1,x2,,xn} be an object et, and A= {a1,a2, an} be an attribute et, = {Ra U U /a A } i a family of equivalence relation on U, Ŕ i an equivalence relation on U, then we can define (U,A,,Ŕ) a a conitent information ytem of our image. To obtain deciion rule, the relationhip between two different familie of relationhip need to be identified [1]. The concept of the image information data will be introduced to find thi connection between the two equivalence relation, which i the bai of deriving the overall relationhip from a family of relationhip. Definition 2: Let S = (U,A,,Ŕ) be our image information ytem; then. If Ra Ŕ then S i conitent information ytem for our image. If RB Ŕ(B A ), where B i an attribute conitent et of S, and any real ubet of B i not attribute conitent et, then B i attribute reduction et of S[4][2]. We can alway get deciion rule for a given Image. However, thee rule are applicable for deciion in partial condition, not for all the combination of different condition. Fortunately, rule fuion can offer a much deciion rule under different condition a poible with a limited number of exiting rule. 3.3 Rule fuion in the Image Information Sytem For an image information ytem (U,A,,Ŕ), Ra Ŕ, if B i an attribute conitent et, then RB Ŕ Suppoe U/RB={[Xi]B / Xi U} U/ Ŕ={D1,D2 Dr} becaue RB Ŕ then, for any Xi Dj, let [Xi]B Dj, then we have a deciion rule: If x [Xi]B, then the deciion i D j. The attribute reduction method i hown a follow [3]: Divide U into equivalence clae uing ŔU/ Ŕ={D 1,D 2, D r } For D j (j<r), denoted M j ={(f 1 (x i ))}, ={(f 2 (x i ))}, ={(fm(x i ))} / [x i ]A D j )} (j r) Union of element in M j, we have (j r) For any, v 1 V1 l r, denoted E = {v 1 }, {v 2 }, {v l } computing get rule: If then. Then we 4. Selection of Fire Alarm Signal Baed on Statitical Characteritic Being concerned about knowing which kind of image attribute and the number of attribute that are elected for different kind of fire, if the elected attribute type i of too mall importance, it i hard to ditinguih different type of fire and non-fire ource. On the other hand, larger type of attribute lead to a lot of data redundancy, and will increae the complexity of the fuion proce. It i well known that fire ha unique viual ignature. Randomne, boundary

3 103 roughne and kewne can be powerful dicriminant fort claification becaue of flickering and random characteritic of fire a tated in [5] which i the reaon why we conider them a the three fire detector parameter in order to collect the fire information for the data fuion. 5. Applicational experiment 5.1 Example data FIGURE 1: Background,with fire and k-mean egmented image Xn record i given by firt egmenting an image with k- mean to find a new object which i then extracted from the background image (figure 1) and the image of the extracted object (figure 3) give the attribute for every Xn record. To judge the performance of the propoed method, we elect a group of real fire to demontrate it capability. When thi group of fire data i expanded, in other word if we get more type of fire detection data, the method can then be ued to fue that data and to derive deciion rule a hown in Table I. Serial No 5.2 Statitical Interpretation U Randomne a1 Table 1.Sample of data record Input Data To calculate the probability of naked fire, according to different tep in the image information ytem fuion, from Table I we get: For the firt tep: U/d 1 ={{X 5,X 11 },{X 7 },{X 3 },{X 6 },Ф,{X 10 },{X 1,X 8,X 12,{X 4 },{X 2 },{X 9 }} Denoted: D 0 = Ф, D 1 = {X 5,X 11 }, D 2 = {X 7 }, D 3 = {X 3 }, D 4 = {X 6 }, D 5 = Ф, D 6 = {X 10 }, D7 = {X 1,X 8,X 12 }, D 8 = {X 4 }, D 9 = {X 2 }, D 10 = {X 9 } D i repreent the probability of naked fire, with a value range from 0.0 to 1.0. For the econd tep: Boundary Roughne a2 Skewne a3 Fire Probability Nake d d1 Smolderin g d2 X X X X X X X X X X M 1 = {(0.0}, {0.1}, {0.3}, {0.1}, {0.1}, {0.0})};M 2 = {(0.0}, {0.0}, {1.0})};M 3 = {(0.1}, {0.2}, {0.0})};M 4 = {(0.0}, {1.0}, {0.0})};M 6 = {(0.2}, {0.3}, {0.0})};M 7 = {({0.1}, {1.0}, {0.0}), ({0.3}, {0.5}, {0.2}), ({0.4}, {0.0}, {0.2})};M8 = {(0.5}, {0.8}, {0.1})};M 9 = {(0.3}, {1.0}, {0.5})};M 10 = {(0.6}, {0.8}, {0.8})} For the third tep, union of element in Mj, we have F 1 = ({0.0,0.1}, {0.1},{0.0,0.3}); F 2 = ({0.0},{0.0},{1.0}); F 3 = ({0.1}, {0.2}, {0.0}; F 4 = ({0.0}, {1.0}, {0.0}; F 6 =({0.2},{0.3},{0.0}; F 7 =({0.1,0.3,0.4},{0.0,0.5,1.0},{0.0,0.2} F 8 = ({0.5},{0.8}, {0.1}; F 9 = ({0.3}, {1.0}, {0.5}; F 10 =({0.6},{0.8},{0.8} For the fourth tep, given E = E=({0.1},{0.8},{0.2}), then D(F 1 /E)=2/5, D(F 2 /E)=0, D(F 3 /E)=1/3, D(F 4 /E)=0, D(F 5 /E)=0, D(F 6 /E)=0, D(F 7 /E)=5/8, D(F 8 /E)=1/3, D(F 9 /E)=0, D(F 10 /E)=1/3

4 104 Finally, we get the following rule : If (a1,0.1) (a2,0.8) (a3,0.2), then D=0.7(5/8), value 5/8 i the confidence of the rule. That i, when the randomne attribute give value 0.1, the boundary roughne give value 0.8, the kewne give value 0.2, and the probability of naked fire i 0.7. Similarly, the probability of the moldering fire can be hown a follow: D(F 1 /E)=3/5, D(F 2 /E)=1/3, D(F 3 /E)=1/3, D(F 4 /E)=1/3, D(F 5 /E)=0, D(F 6 /E)=0, D(F 7 /E)=1/3, D(F 8 /E)=0, D(F 9 /E)=1/3, D (F 10 /E)=0 and we get the following rule If (a 1, 0.1) (a 2, 0.8) (a 3,0.2), then D=0.1 (3/5), value 3/5 i the confidence of the rule. That i, when the randomne attribute give a value of 0.1, the boundary roughne give a value of 0.8, the kewne give a value of 0.2, and the probability of moldering fire i 0.1. To conclude, with the randomne value of 0.1, the boundary roughne value of 0.8, and the kewne value of 0.2, the probability of naked fire i 0.7 while the probability of moldering fire i Dicuion on Reult To interpret the reult of thi method, we conidered that if the probability of an image to contain fire i 0.5 or higher, then the image ha fire. 500 with moldering fire and 500 with naked fire and 1000 without Table 2. Detailed comparative analyi. fire were ued for teting the propoed method. The different environment we ued are candle light, electrical tube light, paper fire and platic fire in night hot and day time hot to gain the variability. Another ource of frame ha been taken from the indoor video clip from Signal and Image Proceing Group at Bilkent Univerity in Turkey ( bilkent.edu.tr/viifire/demo/fireclip/) and we compared the reult with a Background Binary Mak denoted a BBM ued with tatitical color model a it ha been developed on the ame environment[6] and then we compared our reult with a wavelet-baed model of fire frequency ignature denoted a WMF[8] and FDS(Fire detection baed on dicrete coine coefficient)[9]. From the reult preented in Table II, out of 1000 containing fire(poitive ), the WMF, FDS and RSFD counted up to 850,890 and 910 repectively, which mean that RSFD outperform the WMF and FDS by 6% and 2% repectively. Out of 1000 without fire(negative ), WMF, FDS and RSFD counted up to 860, 870 and 900 repectively, which how that RSFD outperform the WMF and FDS by 4% and 3% repectively. From the reult, it i clear that the RSFD baed fire detection preented an advantage of getting more poitive a hown by the three different judging method: accuracy, enitivity and pecificity, which were ued a defined in equation (1), (2) and (3)[9]. Poitive Negative Performance Method Detected poitive Fale negative Detected negative Fale poitive Accuracy Senitivity Specificity WMF[8] 85% 15% 86% 14% FDS[9] 89% 11% 87% 13% RSFD 91% 9% 90% 10% TP TN Accuracy x100% (1) TP TN FP FN Senitivity Specificity TP TP FN TN TN FP (2) (3) Where TP, TN, FP and FN are true poitive, true negative, fale poitive, and fale negative. 6. Concluion Rough et theory being a good mathematical tool ued for data claification, can effectively olve fire fale alarm problem taking image feature a data information into account. After image data i repreented a rough et information data, we can fue the fire detection data and get the reliability of fuion calculation a well. To check the robutne of the ued technique, we compared our reult to thoe from the wavelet-baed

5 105 model of fire frequency ignature (WMF) and FDS(Fire detection baed on dicrete coine coefficient). The averaged overall accuracy i about 5% better than that of WMM and 3% better than that of FDS. The enitivity of 0.89 and pecificity of 0.88 how that thi method can be mart enough for fire detection. Neverthele, computing accuracy i trongly linked to how many ample data we have. A ufficient number of ample et are needed in order to guarantee an improvement of the preciion. Reference [1] Z. Pawlak. Rough Set, International Journal of Computer and Information Science, vol 11, no. 5, 1982, pp [2] Jia-Yang Wang, An Luo, Song-Qiao. Deciion analyi baed on rough et, Fuzzy ytem and Mathematic, vol 20, no. 5, pp [3] Naiwei Cheng, A Deciion-Making Method for Fire Detection Data Fuion Baed on Rough Set Approach International Conference on Intelligent Sytem Deign and Engineering Application [4] Pawlak Z, Slowinki R. Rough et approach to multi attribute deciion analyi European Journal of attribute deciion analyi operation Reearch, : [5] Jenifer P. Effective Viual Fire Detection in Video Sequence Uing Probabilitic Approach Computational Intelligence, Proceeding of ICETECT [6] Akhloufi, L. Roil, L. Abdelhadi and Y. Tion. Dynamic Fire Modeling in Three-dimenional Space, Firt IEEE International workhop on Image Proceing Theory, Tool & Application, pp [7] Zhou Junzhe Reearch on the Technology of Fire Detection Baed on Image Proceing in Unmanned Subtation 2010 Third International Conference on Intelligent Network and Intelligent Sytem. [8] W. Li,B. Fu,L. Xiao, Y. Wang, P. Liu "A Blockbaed Video Smoke Detection Algorithm".Journal of oftware, 2013,8(1)pp [9] J.P. Dukuzumuremyi, Beiji Zou, C.P. Mukamakuza D.Hanyurwimfura and E. maabo. Dicrete Coine Coefficient a Image feature for Fire Detection baed on Computer Viion International Journal of Computer [10] J.Gubbi, S. Maruic, and M. Palaniwami, Smoke Detection in Video uing Wavelet and Support Vector Machine, Fire Safety Journal, Elevier, September 2009, 44(8) pp [11] W. Li,B. Fu,L. Xiao, Y. Wang, P. Liu "A Blockbaed Video Smoke Detection Algorithm".Journal of oftware, 2013, 8(1)pp [12] CHEN Junzhou, YOU Yong, PENG Qiang Dynamic Analyi for Video Baed Smoke Detection IJCSI International Journal of Computer Science Iue, Vol. 10, Iue 2, No 2, March 2013