SVM Based Forest Fire Detection Using Static and Dynamic Features

Transcription

1 DOI: /CSIS Z SVM Based Forest Fre Detecton Usng Statc and Dynamc Features Janhu Zhao, Zhong Zhang, Shzhong Han, Chengzhang Qu Zhyong Yuan, and Dengy Zhang Computer School, Wuhan Unversty, Wuhan, Hube, , PR Chna Abstract. A novel approach s proposed n ths paper for automatc forest fre detecton from vdeo. Based on 3D pont cloud of the collected sample fre pxels, Gaussan mxture model s bult and helps segment some possble flame regons n sngle mage. Then the new specfc flame pattern s defned for forest, and three types of fre colors are labeled accordngly. Wth 11 statc features ncludng color dstrbutons, texture parameters and shape roundness, the statc SVM classfer s traned and flters the segmented results. Usng defned overlappng degree and varyng degree, the remaned canddate regons are matched among consecutve frames. Subsequently the varatons of color, texture, roundness, area, contour are computed, then the average and the mean square devaton of them are obtaned. Together wth the flckerng frequency from temporal wavelet based Fourer descrptors analyss of flame contour, 27 dynamc features are used to tran the dynamc SVM classfer, whch s appled for fnal decson. Our approach has been tested wth dozens of vdeo clps, and t can detect forest fre whle recognze the fre lke objects, such as red house, brght lght and flyng flag. Except for the acceptable accuracy, our detecton algorthm performs n real tme, whch proves ts value for computer vson based forest fre survellance. Keywords: Forest flame, Color segmentaton, Statc feature, Shape matchng, Dynamc feature, SVM. 1. Introducton Currently many nsttutons are tryng to develop relable and effcent methods to forecast the fre dsasters, whch may nduce heavy casualty and property loss as well as serous socal mpact. The tradtonal method to detect fre s employng some people as nspectors, but human resource s expensve and such approach has very low effcency. Fre sensors have already been used as another method to detect the partcles generated by smoke or fre, temperature, relatve humdty, etc. But they must be placed n the proxmty of fre or ther detectng range s usually exceeded, and the approach fals to supply the addtonal nformaton about the process of burnng, such as fre

2 Janhu Zhao et al. locaton, sze, growng rate, and so on. Fortunately, computer vson based fre detecton brngs us a new knd of method whch can overcome the key defcences of the aforementoned methods. However, ths new approach stll remans mmature, and many tough problems exst n ts 3 man stages: mage segmentaton, target trackng and object classfcaton. What s more, one certan detecton algorthm cannot work well for all knds of fre dsasters, e.g. tunnel fre, buldng fre or forest fre. Movng objects estmaton s often used to segment the possble fre regon from vdeo sequence, and two man tradtonal algorthms nclude consecutve frames subtracton and background subtracton [1-6]. In the algorthm of consecutve frames subtracton, transent change of mage can be detected, but the overlappng regon of two consecutve frames can be mstakenly taken as background. In the algorthm of background subtracton, ntact target regon can be extracted because of the statc state of the background mage, but the extracted target may be vague and naccurate f the background mage cannot be updated n tme. For forest envronment, the whole scene does not keep stll due to wavng trees, changng weather, varyng lght, movng shadow, shakng camera, and so on. Therefore, compared wth movng estmaton, color based segmentaton s more sutable for forest fre extracton. Celk et al. [7] descrbed the color features of fre n RGB color space, and decded whether one pxel belongs to the fre regon usng rules represented by two groups of nequatons. Chen et al. [8] tred to segment the fre regon from one mage n RGB color space based on three deduced decson rules, wthn whch the saturaton value of each extracted possble fre pxel needs to be more than one threshold value n order to exclude the other fre-lke regons. Celk and Demrel [9] proposed a generc color model for flame pxels classfcaton n YCbCr color space wth several rules, and they used three polynomals to model the regon contanng fre pxels from 1000 sample mages n CbCr chromnance plane. Phllps et al. [10] used test data where the fre has been solated manually to create a color lookup table by creatng a Gaussan smoothed color hstogram to detect the fre colored pxels, thus ther approach based upon tranng s scene specfc wth ncreased accuracy f tranng sequences are avalable for specfc knds of fres. Toreyn et al. [11] obtaned the fre color dstrbuton from sample mages, and represented the 3D pont cloud n RGB color space usng a mxture of Gaussans, then the pxel wth color value nsde one of the dstrbuted Gaussan spheres s assumed to be a fre colored pxel. Krstnc et al. [12] compared the lookup table method and the probablstc model method, and ther experments proved that lookup table classfer acheves the lower performance. Further valdatons [13,14] have been performed based on fre color based segmentaton to mprove the extracton accuracy. Besdes color nformaton, dynamc features of fre are sgnfcant clues used to dstngush fre from other fre-lke objects [15-21]. To dentfy a fre s growth, sze varaton of fre area s calculated from some consecutve vdeo frames [17]. If the number of extracted fre pxels s ncreasng wth tme and greater than the explctly defned threshold, a fre alarm wll be gven. Flckerng frequency s an mportant clue for fre snce flames flcker wth a 822 ComSIS Vol. 8, No. 3, June 2011

3 SVM Based Forest Fre Detecton Usng Statc and Dynamc Features characterstc frequency of around 10 Hz ndependent of burnng materal and burner, and there are several approaches to compute fre flckerng frequency [18-20]. Zhang et al. [18] thought the heght of flame s changeable due to flame flcker and the changng pattern dffers from the jammng sources, so the change of flame heght s taken as a dynamc feature. Yuan et al. [19] drectly utlzed the temporal varaton of flame contour as clues for detectng whether a pxel s fre or not n vdeo mages. The stochastc characterstcs of fre moton are estmated by an autoregressve model of changes n Fourer coeffcents of the regon boundary [20], and temporal changes of the coeffcents are used as the sgnatures of fre. Toreyn et al. [11] kept on trackng the hstory of red channel for each pxel whch s part of fre contour n a relatve short tme, and took them as the nput of wavelet method. Apart from the changng of fre regon's area and flcker, Hu et al. [21] employed the changng of fre regon's roundness whch descrbes complexty of the shape, to help flter the regons wth regular shape. Dfferent wth the other knds of fre survellances, forest fre montorng has ts own propertes. The cameras are usually nstalled on the top of mountans, and they are not very stable because of wnd blow. Vew range of the cameras s relatvely wde, can be 3-5 km generally or even about 8 km. Focal length of the cameras s changeable, and the sze of objects n recorded mages s not constant. Most of the publshed papers worked on detecton and analyss of sole fre regon, but n forest fre, there may be more than one flame regons n the montored area. All of them have caused a great deal of trouble for vson based fre detecton, therefore t s necessary to specally study the case of forest fre recognton. Our proposed forest fre detecton algorthm consders statc and dynamc features subsequently. The rest of our paper s organzed as: color based segmentaton ncludng 3D color model wth GMM and colors labelng wth new flame pattern defnton are provded n secton 2, computaton of statc features and SVM based classfcaton are descrbed n secton 3, shape based matchng of multple regons among contnuous vdeo mages s gven n secton 4, dynamc features computaton and based fnal determnaton wth SVM are provded n secton 5, expermental results on mages and vdeos are descrbed n secton 6, and the concluson s gven n secton Color Based Fre Segmentaton D Color Model For segmentaton of possble flame regons, color values of each pxel n an mage are checked wth a pre-determned color dstrbuton, whch represents the range of possble fre colors n a color model such as RGB space. As shown n Fg. 1, there are 530,000 flame pxels segmented manually from the ComSIS Vol. 8, No. 3, June

4 Janhu Zhao et al. fre regons of 23 sample mages. Of course, the threshold values along R, G and B axs can be used to defne a rough space for fre color. To buld a more precse color model, 3D shape of the pont cloud s represented by Gaussan mxture model (GMM), and the pxel whose color wthn the range of the GMM dstrbuton model can be taken as a canddate fre pxel. Fg. 1. 3D pont cloud of sample fre pxels Frst, we use expectaton maxmzaton (EM) algorthm to tran the GMM parameters: the weght values, the center, and the covarance matrx. Then, whether one pxel belongs to flame regon of the mage under processng can be decded by calculaton of ts probablty wth the followng formula: g( x;, ) 1 1 T 1 exp ( x ) ( x ) d (2 ) 2 (1) p( x) * g( x;, ) (2) The weghtng value, center and covarance matrx of each Gaussan model are, and respectvely. The probablty p(x) llustrates how close of pont x to the fre regon. The number of Gaussan models n GMM can be manually assgned or automatcally computed. We tred automatc approach n experments and the calculated best number of Gaussan models s 8. Based on the traned 3D color dstrbuton model, pxels of one mage are checked one by one, and then the possble flame regons wthn the mage can be segmented automatcally. 824 ComSIS Vol. 8, No. 3, June 2011

5 SVM Based Forest Fre Detecton Usng Statc and Dynamc Features 2.2. New Defnton for Forest Flame Pattern Ref. [20] studed color, geometry and moton of fre for recognton, and modeled the fre regon n a sngle mage as: (1) t stands n hgh contrast to ts surroundngs; (2) t exhbts a structure of nested rngs of colors, changng from whte at the core to yellow, orange and red n the perphery. Ths descrpton gves a standard to detect fre. However, wth respect to the case of forest flame, many fre regons do not have the structure of obvous nested rngs, as shown n Fg. 2, and mostly because that they fal to burn fully. Fg. 2. Fre regons wth non-sgnfcant nested structure Therefore, for forest fre, we present a new defnton to descrbe the flame pattern more properly: (1) the perphery of fre regon s n orange or red color; (2) only f the fre burns fully, there are one or more whte-yellow color cores Labelng Three Types of Colors Based on our new defnton for forest fre pattern, there may be three types of colors n the segmented fre regon: whte-yellow, orange, red. Thus the pxels n fre can be labeled wth three correspondng marks. As pxels wth whteyellow color belong to the hgh brght flame regons, V value of the HSV color space s employed to help label such pxels. Snce flames are often covered wth smokes n forest, ts brght value wll be decreased wth dfferent degrees. In ths case, we can not use a fxed V value as the threshold. Therefore, an algorthm s proposed to self-adaptvely calculate the threshold value of V for fre mages, and the algorthm s descrbed as follows. Two threshold values, V low and V hgh, are defned for V based segmentaton. The lower threshold V low s a constant value from our expermental experence, whle the hgher threshold V hgh s a value computed automatcally. Related wth V low and V hgh, there are two subset of one mage: L, set of the pxels whose V value s no less than V low ; H, set of the pxels whose V value s no less than V hgh. L { p p [ Vlow,255]} (3) H { p p [ V,255]} hgh ComSIS Vol. 8, No. 3, June

6 Janhu Zhao et al. Obvously, H s a subset of L. If L s taken as the possble fre regon, H can be used for further determnaton of fre cores. For the number of pxels, the percentage of H n L can be controlled by a parameter α Num( H) (4) Num( L) The smaller the value of α, the less the number of pxels n H, together wth the hgher threshold value of V hgh. The procedure to compute V hgh s: Step 1: Defne the dstrbuton functon F(x) for subset L as v( p ) F ( v( p )) P( t v( p )) f ( t), p L (5) t Vlow Step 2: Fnd the subset H based on F(x) as H { p F( v( p )) 1 } (6) Step 3: Calculate the hgher threshold value V hgh as V hgh mn( v( p ) p H) Once we get the hgher threshold value V hgh, we label the pxels as whteyellow f ther V value exceeds V hgh. For the left pxels of the canddate fre regon, we use the experental value of H and S ranges to decde whether they belong to orange or red. (7) 3. Statc Features and SVM Classfer After color based segmentaton, the possble flame regons are obtaned n one sngle mage. In our method, they are not drectly used as fre areas, but are further checked to flter out the false canddates based on some statc features wth traned support vector machne (SVM). The statc features nclude color dstrbuton, texture parameter and shape roundness Statc Features from Sngle Frame (1) Color dstrbuton (5 features) Durng color segmentaton wth our new flame pattern, regons wth whteyellow, orange and red color are labeled. We calculate the rato of the pxels number n each labeled color to the pxels number n the entre canddate fre regon. The rato of whte-yellow pxels (Eq. 8), the rato of red pxels (Eq. 9) and the rato of orange pxels (Eq. 10) are all statc features of forest fre. 826 ComSIS Vol. 8, No. 3, June 2011

7 SVM Based Forest Fre Detecton Usng Statc and Dynamc Features num( whte yellow ) Rato WY (8) num( entre regon) num( red) Rato R (9) num( entre regon) num( orange) Rato O (10) num( entre regon) Wth respect to each canddate fre regon, ther color hstograms n dfferent color channels are computed respectvely. Suppose I s one gray value n one color channel, Ng s the number of gray levels, P(I) s the rato of the number of pxels wth I value to the number of pxels n the canddate regon, the expectaton of Eq. (11) and the varance of Eq. (12) are computed as statc features, e.g. two features for H color channel. (2) Texture parameter (5 features) Ng 1 I 0 e E[ I] I * P( I) (11) Ng 1 I var E [( I E[ I]) ] ( I e) P( I) (12) Forest fre also has texture features [22-24], thus we can extract the texture parameters from each canddate regon and then consder them n decson. Snce H value represents the color nformaton n HSV color space, the cooccurrence matrx of the regon s H channel s employed to descrbe the texture feature. From experments, t can be found that only the co-occurrence matrxes wth zero degree have evdent dfferences between fre and non-fre regons, thus parameters of zero degree co-occurrence matrx s used. Among parameters of the N*N co-occurrence matrx, the angular second moment of Eq. (13), the entropy of Eq. (14), the mean of Eq. (15), the contrast of Eq. (16) and the nverse dfference moment of Eq. (17) are chosen as statc features. Angular Second Moment Entropy E Mean N 1 N 1 N 1 0 j 0 M ASM N 1 N 1 0 j 0 2 p (, j) (13) p (, j) log p(, j (14) N Contrast C n 0 2 ) p ( ) (15) N 1 N 1 n p(, j), - j n (16) 0 j 0 ComSIS Vol. 8, No. 3, June

8 Janhu Zhao et al. Inverse Dfference Moment (3) Shape roundness (1 feature) IDM N 1 N 1 0 j 0 p (, j)/[1 ( j) 2 ] (17) Gven a segmented canddate fre regon, we retreve ts boundary usng the classcal Laplacan operator of 1 4 1, then t s convenent to compute the 8-connected boundary chan code [25,26] for the regon, as llustrated n Fg. 3. From the chan code, t s easy to calculate area S of the regon and permeter L of the boundary. Accordngly, we compute the shape roundness as L 2 /S, whch can descrbe the complexty of shape,.e. more complex shape has larger soundness value. Shape roundness helps to get rd of the canddate regons less complex than fre, e.g. regular red car n mage. Fg. 3. Canddate regon wth ts boundary chan code 3.2. SVM Classfer wth Statc Features Support vector machne (SVM) s a set of related supervsed learnng methods that analyze data and recognze patterns, thus t s employed n our method for features based classfcaton. The open source package (LIBSVM) s used to construct a two-class SVM classfer. To tran the SVM, the above 11 statc features are computed and collected from sample mages wth real fre or fre lke objects. Wth the help of these features and radal bass functon kernel, we can obtan the man parameters C= and = for SVM. Therefore, the segmented canddate fre regons are further checked by the traned SVM classfer, and the false regons can be deleted. Of course, statc features can help flter the canddate regons segmented from one 828 ComSIS Vol. 8, No. 3, June 2011

9 SVM Based Forest Fre Detecton Usng Statc and Dynamc Features sngle mage, but are not enough to descrbe the forest fre changng n vdeo sequences. 4. Shape Based Flames Matchng Before computng dynamc characterstcs of varyng fre, the correspondng canddate fre regons should be found among consecutve vdeo frames, whch s a problem of pattern matchng. Although the camera may mldly wobble and the canddate flame regons may randomly flcker, locatons and shapes of the correspondng canddate regons among consecutve vdeo frames do not change serously. Therefore, two parameters of overlappng degree and varyng degree are defned to evaluate the matchng of two regons n our approach. Suppose R1 and R1 (or R2 and R2 ) are the correspondng regons (there may be multple matchng pars) n two neghbor frames, as llustrated n Fg. 4. Fg. 4. Regon matchng of consecutve frames The overlappng degree of two regons s S( R1 R1'), 0 1 (18) max( S( R1), S( R1')) where S(R 1 R 1 ) represents the overlappng area of regon R 1 and regon R 1, max(s(r 1 ), S(R 1 )) represents the larger area of R 1 and R 1, and α s an experental value,.e. the two correspondng regons are more lke the same flame wth the larger value of overlappng degree. The varyng degree of two regons s S( R1) S( R1'), 0 1 (19) mn( S( R1), S( R1')) where S(R 1 ) represents the area of regon R1, S(R 1 ) represents the area of regon R 1, mn(s(r 1 ), S(R 1 )) represents the smaller area of R 1 and R 1, and β s an experental value,.e. the two correspondng regons are more lke the same flame wth the less value of varyng degree. ComSIS Vol. 8, No. 3, June

10 Janhu Zhao et al. Fg. 5. Matchng results of fve fres from contnuous frames The proposed defntons for flames matchng are tested by some collected vdeo clps. As shown n Fg. 5, the frst row dsplays the source mages, the second row refers to results from color segmentaton, the thrd row llustrates the matchng results. In the matched results, fre regons wth the same color mean that they are the correspondng regons of the same flame. Fg. 6. Detecton of the breakng behavor Behavors of forest fre are very complex wth tme, e.g. one fre regon can slowly break nto several small parts, or the small fres can burn nto one regon. Our matchng algorthm has the ablty to detect such changes. As llustrated n Fg. 6, one flame dvdes nto several small regons, and the small parts cannot match wth the whole fre. In ths case, the number of canddate fre regons ncreases and the matchng operaton s performed on the new set of regons. 830 ComSIS Vol. 8, No. 3, June 2011

11 SVM Based Forest Fre Detecton Usng Statc and Dynamc Features 5. SVM Determnaton wth Dynamc Features Based on the matched results, dynamc features of the canddate fre regons from contnuous vdeo frames can be extracted, and used to further dentfy forest fre from the other fre lke objects. In our method, the dynamc features nclude the varatons of color dstrbuton, texture, roundness, area, contour and the flckerng frequency Dynamc Features from Matched Regons (1) Varaton of color dstrbuton (5 features) It s defned as the varaton of color dstrbutons (from Eq. 8 to Eq. 12 n secton 3.1) of one canddate regon among a sequence of vdeo frames. (2) Varaton of texture (5 features) It s defned as the varaton of texture parameters (from Eq. 13 to Eq. 17 n secton 3.1) of one canddate regon among a sequence of vdeo frames. (3) Varaton of roundness (1 feature) It s defned as the varaton of shape roundness (n secton 3.1) of one canddate regon among a sequence of vdeo frames. (4) Varaton of area (1 feature) It s defned as the varaton of area of one canddate regon among a sequence of vdeo frames. Area s represented by the number of fre pxels n one regon, and the area of forest fre s contnuously changng snce fre s an nstable and developng procedure. (5) Varaton of contour (1 feature) It s defned as the varaton of contour of one canddate regon among a sequence of vdeo frames. Snce the shape of fre regon s changeable owng to ar flowng, we can calculate the contour fluctuaton to measure the dsorder. Assume there are N ponts on the boundary, and they are expressed n the complex form { z z x jy}, where ( x, y ) are the coordnates of the th pont on the boundary traversed clockwse, as shown n Fg. 3. Coeffcents of the dscrete Fourer transform (DFT) [26,27] of z are then calculated as F w 1 N N 1 2 z exp( j w) N (20) where 0 F represents the centre of gravty of the transformed 1D boundary, whch does not carry shape nformaton, so we neglect t to acheve the ComSIS Vol. 8, No. 3, June

12 Janhu Zhao et al. translaton nvarance. Experments show that only a few dozens of the Fourer coeffcents are really needed to descrbe the contour, thus the front 32 ones ' ' ' 1 2 F D ( F, F,..., ) are used, and the dfference of two consecutve 2 Fourer descrptors correspondng to two neghbor frames s defned as 32 ' 1 F ' w Fw w D (21) If D s greater than Td and lasts for a tme perod longer than Tm, where Td and Tm are statstcal threshold values from experments, t means that there s a drastc change n shape and the regon s probably a fre. (6) Flckerng frequency (1 feature) Flckerng frequency s another mportant clue for forest fre snce the flames flcker wth a characterstc frequency around 10 Hz. We compute the varance of every two consecutve Fourer descrptors n a relatvely short tme and then analyze the sequence of varances wth temporal wavelet. The vdeo capturng rate should be hgh enough to capture flame flckerng,.e. at least 20 Hz to deal wth the 10 Hz fre flckerng. In our experment, the dgtal camera can capture 30 frames per second, so t works. As shown n Fg. 7, x n [k, l] represents the varance of Fourer descrptors between the nth and the (n+1)th frame, and each x n [k, l] n a relatvely short tme s assgned to a two-stage flter bank. The two-channel decomposton flter s consttuted of hgh-pass flter (HPF, {-0.25, 0.5, -0.25}) and low-pass flter (LPF, {0.25, 0.5, 0.25}). If there s hgh frequency varaton, hgh-band sub-sgnals d n and e n should be non-zero value. On the contrary, f the nth frame stay statonary compared wth the consecutve frame, these two subsgnals should be equal to zero or very close to zero due to the hgh-pass flters. Thus the number of zero crossngs of the sub-band sgnals d n and e n n one perod s used as fre flckerng frequency. Fg. 7. A two-stage flter wth HPF and LPF 832 ComSIS Vol. 8, No. 3, June 2011

13 SVM Based Forest Fre Detecton Usng Statc and Dynamc Features 5.2. SVM Classfer wth Dynamc Features Snce the flckerng frequency s a constant value of about 10 Hz, t can be drectly used as one dynamc feature of forest fre. For color dstrbuton, texture, roundness, area and contour, varatons of them (13 features) from n consecutve mages are computed and then taken as dynamc features. To make sure that fre detecton performs n real tme and gves alarms wthout delay, n should be a relatve small number. Based on the fact that the flames flcker around 10 Hz and the recorded vdeos have 30 frames per second, n s assgned wth the value of 20. That s, dynamc features are computed for the forest fre from every 20 consecutve vdeo frames. Therefore, an n*m matrx s constructed for fre features of a vdeo clp, where n=20 whle m=13 s the number of aforementoned dynamc features. Suppose X (, j) s one element of the matrx correspondng to the th vdeo frame and the jth fre feature, dynamc features based on the matrx are defned as the average and the mean square devaton n 1 E( j) X (, j) (22) n 1 1 n 1 2 S( j) ( X (, j) E( j)) (23) n Therefore, for any vdeo clp, there are 2*13=26 varyng dynamc features,.e. the average and the mean square devaton of color dstrbuton, texture, roundness, area and contour. Together wth the flckerng frequency, the 27 dynamc features are used as nput of SVM classfer, and the traned SVM s appled for the fnal decson. 6. Expermental Results We developed our algorthm wth C++ and Open CV under VC.NET n Wndows XP, and tested t on a lot of vdeo clps wth real fres or fre lke objects. As our algorthm ncludes color based segmentaton and SVM based statc and dynamc classfcatons, not only the fnal recognton, but also the ntermedate results are dsplayed and analyzed Results of Color Based Segmentaton As shown n Fg. 8, vdeo frames (the 1st, 2nd and 3rd columns) and stll mages (the 4th column) n the 1st row are collected and used as the expermental data. For comparson, the color based segmentaton method of ComSIS Vol. 8, No. 3, June

14 Janhu Zhao et al. Ref. [7] s tested frst. Results from the frst group of nequatons n [7] wth relatve loose lmts are shown n the 2nd row, and t can be found that many other regons reman as they have the smlar color as fre. Results from the second group of nequatons n [7] wth relatve strct lmts are shown n the 3rd row, and t can be found that only a few real fre regons are segmented. Fg. 8. Segmentaton results from [7] Then another color based segmentaton method of Ref. [8] s tested based on ts three deduced decson rules. In ther method, parameters RT and ST of the decson rules must be set manually and they exert crucal nfluence on fre segmentaton, especally for parameter ST. As shown n Fg. 9, the 1st row dsplays the segmented results from RT=170 and ST=0.3, whle the 2nd row dsplays the segmented results from RT=170 and ST=0.9. Fg. 9. Segmentaton results from [8] 834 ComSIS Vol. 8, No. 3, June 2011

15 SVM Based Forest Fre Detecton Usng Statc and Dynamc Features The same mages are used to test our method, as shown n Fg. 10. From the expermental results t can be found that our algorthm has the ablty to segment fre regons more precsely from the monocular mages, and the regons wth whte-yellow, orange, red colors are also labeled respectvely llustrated wth dfferent gray value,.e. the segmented regons from our approach has more nformaton. Of course, the segemented results are only possble fre regons, and they need further determnaton. Fg. 10. Segmentaton results from our method Fg. 11. Segmentaton results of fre colored objects Our method s also tested on some mages wth fre colored non-fre objects such as brght lght, flyng red flag and movng red car. From Fg. 11, t can be found that the objects wth fre lke color can be taken as possble fre regons f usng only color based segmentaton, whch also proves the necessty to take the other fre features nto consderaton except for color Determnaton wth Statc and Dynamc Features The statc features defned n our method are used for further determnaton wth the help of SVM classfer. Results of Fg. 12 llustrate that the statc features can help remove a lot of fre lke regons snce there are dfferences between ther color dstrbutons, texture parameters or shape roundness and those of real fre. But for the flyng red flag, even ts statc features are very smlar wth forest fre, thus the flag stll remans after statc SVM. Then the dynamc SVM classfcaton s performed on contnuous vdeo frames wth the dynamc features ncludng varatons of color dstrbuton, texture, roundness, ComSIS Vol. 8, No. 3, June

16 Janhu Zhao et al. area, contour and the flckerng frequency. After dynamc SVM, the flyng red flag s recognzed and fltered. Fg. 12. SVM determnaton of fre lke regons Fg. 13. SVM determnaton of real fre regons Wth the same parameters, SVM classfcatons usng statc and dynamc features are consequently performed on the segmented results of Fg. 10 wth real forest fre. Snce the mage of the 4th column n Fg. 10 s a sngle mage and dynamc SVM cannot be appled, only the classfcaton results of the frames from vdeo clps (the 1st, 2nd and 3rd columns) are shown n Fg. 13. It can be found that the man fre regons are detected successfully, but the small fre regons may be fltered. The reason s that there are fre features n the small regons represented by only a few pxels, but the features are relatve weak compared wth the man regons. 836 ComSIS Vol. 8, No. 3, June 2011

17 SVM Based Forest Fre Detecton Usng Statc and Dynamc Features 6.3. Flame Detecton from Vdeo Clps Ref. [10] also presented a complete procedure for vdeo based automatc fre detecton, so the method s mplemented and compared wth our approach. The experments are executed on dozens of vdeo clps, and 8 of them are llustrated n Fg. 14. Data of the frst row are vdeos from F1 to F4 wth forest fre, whle data of the second row are vdeos from N1 to N4 wth fre smlar objects. For vdeo clps wth fre, F1 s early fre, F2 s fully burnng fre, F3 and F4 are fre covered wth thn smoke. For vdeo clps wthout fre, N1 s a movng red car, N2 s a red house captured wth shakng camera, N3 s a brght drvng lght, and N4 s a red flag flyng n the wnd. That s, the data can test the detecton performance under dfferent stuatons. Fg. 14. Fre detecton from vdeo clps Table 1. Performance comparson between Ref. [10] and our method Vdeos Total frames Fre frames Alarm frames of Ref. [10] Alarm frames of our method Alarm rato (%) of Ref. [10] Alarm rato (%) of our method F F F F N N N N The performance comparsons between Ref. [10] and our approach are shown n Table 1. The 1st column lsts the names of vdeo chps n our experment; the 2nd column and the 3rd column lst the total frames and real fre frames respectvely; the rest columns lst the alarm frames and alarm ratos from two compared methods. For vdeo clps wth real fre, our method ComSIS Vol. 8, No. 3, June

18 Janhu Zhao et al. gves better detecton accuracy wth more alarm ratos. For vdeo clps wth fre lke objects, our method provdes hgher recognton precson wth less alarm ratos. Of course, our approach has lower accuracy for fre wth small regons (F1), and the performance s even worse for small fres covered by smoke (F4). Our algorthm runs on a PC wth a CPU C1.7G and 512M DDR RAM, and has a speed of fps. Therefore, except for the acceptable accuracy, our method can perform n real tme. 7. Concluson In ths paper, a new SVM based approach s proposed for forest fre detecton wth both statc and dynamc features. Compared wth the publshed related works, our novel method has the followng advantages: (1) In color based segmentaton, after GMM constructon from sample pxels and segmentaton of canddate fre regons, we defne the specfc forest flame pattern and then label three types of colors ncludng whteyellow, orange and red. The labelng ntroduces a novel feature of forest fre,.e. color dstrbuton, whch s very helpful for further classfcaton. (2) For the segmented results from sngle frame, SVM traned on 11 statc features s appled to flter out the false regons, and only the remaned regons contnue wth the followng steps. In ths way, computatonal expense s saved obvously. (3) Not only the sole target, but multple canddate fre regons are tracked by shape based matchng among the consecutve frames. Wth our defned overlappng degree and varyng degree, the matchng algorthm can also detect complex fre behavors, e.g. one fre regon slowly breaks nto several small parts, or the small fres burn nto one regon. (4) To compute the fre flckerng frequency based on regon contour, the temporal wavelet s used to analyze Fourer descrptors representng the varaton of flame contour n a short perod. Our approach avods explctly settng the threshold value n the exstng FFT methods, whle detects forest fre more accurately than the methods usng wavelet transformaton only. (5) A total of 27 dynamc features are consdered for SVM based fnal classfcaton, and the features are computed from every 20 consecutve vdeo frames. Therefore, except for accuracy, the detecton algorthm can perform and gve alarms n real tme. Our work has been tested wth a lot of real vdeo clps and the expermental results have proved ts effcency. However, for fre wth small regons or fre regons covered wth smoke, there are relatve poor statc and dynamc fre features, and thus the detecton accuracy s stll low. In the future, we wll try the other ways for such problem, e.g. segmentng smoke frst, and consderng both fre and smoke together. 838 ComSIS Vol. 8, No. 3, June 2011

19 SVM Based Forest Fre Detecton Usng Statc and Dynamc Features Acknowledgments. Ths work was supported by Hube Provncal Natural Scence Foundaton of Chna, Natonal Basc Research Program of Chna (973 Program, No. 2011CB707904), Fundamental Research Funds for the Central Unverstes, Research Foundaton (No. AISTC2008_16) from the State Key Laboratory of Aerospace Informaton Securty and Trusted Computng of Mnstry of Educaton, and 985 Project of Cogntve and Neural Informaton Scence, Wuhan Unversty (No ). References 1. La C.L., Yang J.C., Chen Y.H.: A Real Tme Vdeo Processng Based Survellance System for Early Fre and Flood Detecton. Instrumentaton and Measurement Technology Conference, pp (2007) 2. Toreyn B.U., Cetn A.E.: Onlne Detecton of Fre n Vdeo. IEEE Conference on Computer Vson and Pattern Recognton (CVPR), pp (2007) 3. Yuan F.N.: A fast accumulatve moton orentaton model based on ntegral mage for vdeo smoke detecton. Pattern Recognton Letters, 29, pp (2008) 4. Han D., Lee B.: Flame and smoke detecton method for early real-tme detecton of a tunnel fre. Fre Safety Journal, 44, pp (2009) 5. Gunay O., Tasdemr K., Toreyn B.U., Cetn A.E.: Vdeo based wldfre detecton at nght. Fre Safety Journal, 44, pp (2009) 6. Yu C., Fang J., Wang J., Zhang Y.: Vdeo Fre Smoke Detecton Usng Moton and Color Features. Fre Technology, 46(3), pp (2009) 7. Celk T., Demrel H., Ozkaramanl H., Uyguroglu M.: Fre Detecton n Vdeo Sequences usng Statstcal Color Model. IEEE Internatonal Conference on Acoustcs, Speech and Sgnal Processng, pp. II (2006) 8. Chen T.H., Wu P.H., Chou Y.C.: An Early Fre-Detecton Method Based on Image Processng. IEEE Internatonal Conference on Image Processng (ICIP), pp (2004) 9. Celk T., Demrel H.: Fre Detecton n Vdeo Sequences usng a Generc Color Model. Fre Safety Journal, 44(2), pp (2009) 10. Phllps Ⅲ W., Shah M., Lobo N.V.: Flame recognton n vdeo. Pattern Recognton Letters, 23, pp (2002) 11. Toreyn B.U., Dedeoglu Y., Gudukbay U., Cetn A.E.: Computer vson based method for real-tme fre and flame detecton. Pattern Recognton Letters, 27, pp (2006) 12. Krstnc D., Stpancev D., Jakovcevc T.: Hstogram-based smoke segmentaton n forest fre detecton system. Informaton Technology and Control, 38(3), pp (2009) 13. Martnez-de Dos J.R., Arrue B.C., Ollero A., Merno L., Gomez-Rodrguez F.: Computer vson technques for forest fre percepton. Image and Vson Computng, 26, pp (2008) 14. Ko B.C., Cheong K.H., Nam J.Y.: Fre detecton based on vson sensor and support vector machnes. Fre Safety Journal, 44, pp (2009) 15. Toreyn B.U., Dedeoglu Y., Cetn A.E.: Flame detecton n vdeo usng hdden Markov models. IEEE Internatonal Conference on Image Processng (ICIP), pp (2005) 16. Celk T., Demrel H., Ozkaramanl H.: Automatc fre detecton n vdeo sequences. European Sgnal Processng Conference, pp (2006) ComSIS Vol. 8, No. 3, June

20 Janhu Zhao et al. 17. Chen T.H., Kao C.L., Chang S.M.: An Intellgent Real-Tme Fre-Detecton Method Based on Vdeo Processng. IEEE 37th Internatonal Carnahan Conference on Securty Technology, pp (2003) 18. Zhang J.H., Zhuang J., Du H.F.: A New Flame Detecton Method Usng Probablty Model. Internatonal Conference on Computatonal Intellgence and Securty, pp (2006) 19. Yuan F.N., Lao G.X., Zhang Y.M., Lu Y.: Feature Extracton for Computer Vson Based Fre Detecton. Journal of Unversty of Scence and Technology of Chna, 36(1), pp (2006) 20. Lu C.B., Ahuja N.: Vson based fre detecton. 17th Internatonal Conference on Pattern Recognton (ICPR), pp (2004) 21. Zhang D.Y., Hu A.K., Rao Y.J., Zhao J.M., Zhao J.H.: Forest Fre and Smoke Detecton Based on Vdeo Image Segmentaton. SPIE Pattern Recognton and Computer Vson, pp H H-7. (2007) 22. Ferrar R.J., Zhang H., Kube C.R.: Real-tme detecton of steam n vdeo mages. Pattern Recognton, 40, pp (2007) 23. Cremers D., Rousson M., Derche R.: A Revew of Statstcal Approaches to Level Set Segmentaton - Integratng Color, Texture, Moton and Shape. Internatonal Journal of Computer Vson, 72(2), pp (2007) 24. Lu X.W., Wang D.L.: Image and Texture Segmentaton Usng Local Spectral Hstograms. IEEE Transactons on Image Processng, 15(10), pp (2006) 25. Arrebola F., Bandera A., Camacho P., Sandoval F.: Corner Detecton by Local Hstograms of Contour Chan Code. Electroncs Letters, 33(21), pp (1997) 26. Zhang Z., Zhao J.H., Zhang D.Y., Qu C.Z., Ke Y.W., Ca B.: Contour Based Forest Fre Detecton Usng FFT and Wavelet. Internatonal Conference on Computer Scence and Software Engneerng, pp (2008) 27. Zhang D.Y., Han S.Z., Zhao J.H., Zhang Z., Qu C.Z., Ke Y.W., Chen X.: Image Based Forest Fre Detecton Usng Dynamc Characterstcs Wth Artfcal Neural Networks. Internatonal Jont Conference on Artfcal Intellgence, pp (2009) Janhu Zhao receved the B.Sc. degree n Computer Engneerng from Wuhan Unversty of Technology n 1997, the M.Sc. degree n Computer Scence from Huazhong Unversty of Scence and Technology n 2000, and the Ph.D. degree n Computer Scence from Nanyang Technologcal Unversty n From 2003 to 2006, he worked as a Research Assstant/Assocate n Hong Kong Unversty of Scence and Technology. Currently he s workng as an Assocate Professor n Computer School of Wuhan Unversty. Hs research nterests nclude dgtal mage processng and computer graphcs. Zhong Zhang s a graduate student n Computer School of Wuhan Unversty, and hs research nterests are mage processng and pattern recognton. 840 ComSIS Vol. 8, No. 3, June 2011

21 SVM Based Forest Fre Detecton Usng Statc and Dynamc Features Shzhong Han s a graduate student n Computer School of Wuhan Unversty, and hs research nterests are pattern recognton and machne learnng. Chengzhang Qu s a PhD canddate n Computer School of Wuhan Unversty, and hs research nterests are mage processng and computer vson. Zhyong Yuan s workng as an Assocate Professor n Computer School of Wuhan Unversty. Hs research nterests nclude dgtal mage processng and computer graphcs. Dengy Zhang s workng as a Professor n Computer School of Wuhan Unversty. Hs research nterests nclude embedded system desgn, mage processng and pattern recognton. Receved: October 12, 2010; Accepted: January 17, ComSIS Vol. 8, No. 3, June

22