Open Access Early Fire Smoke Image Segmentation in a Complex Large Space

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1 Send Orders for Reprnts to The Open Constructon and Buldng Technology Journal, 2015, 9, Open Access Early Fre Smoke Image Segmentaton n a Complex Large Space Hu Yan 1,2,*, Wang Huqn 1,2, Zhao Qan 3 and Lu Yng 2 1 School of Management, X an Unversty of Archtecture and Technology, X an, Shaanx, , Chna; 2 School of Informaton and Control Engneerng, X an Unversty of Archtecture and Technology, X an, Shaanx, , Chna; 3 X'an Modern Control Technology Research Insttute, X an, Shaanx, , Chna Abstract: Amng at the problem that the fre smoke mage segmentaton algorthm cannot obtan ntegral smoke nformaton, the adaptve background updatng smoke foreground object extracton algorthm based on block segmentaton s proposed. Accordng to the characterstcs that the smoke nternal pxels contnuously roll n the heat of the drver and the pxel concentraton from near the fre source to edge decreases n turn, the method for dfferent blocks usng dfferent dfference threshold s adopted to ensure the ntegrty of the extracted foreground target. Under the dfference between the current frame and the background frame, block dfference thresholds are updated to let the dfferental threshold adjustment wth contnuous adaptve montorng. In the new weghted average updated background mage corrected by the orgnal background mage, the nterference suspcous target edge pxels are removed. Smulaton expermental results show that the method s able to extract more complete nformaton of smoke n a suspected area of the edge, and elmnate the nterference of lght and pedestran n complex space. Keywords: Background updatng, blockng, complex large space, connected doman, smoke mage segmentaton. 1. INTRODUCTION The smoke produced n the smolderng stage s a very sgnfcant character of early fre. Because the smoke affected by plume effect s not sheltered by an obstacle, the analyss and recognton of smoke mage can help acheve early realzaton of the fre warnng [1]. Accurate segmentaton of smoke mage s the premse and foundaton for feature extracton and target recognton. However, compared wth the flame mage, smoke mage features, such as color, gradent, etc. are not clear, and more vulnerable to the outsde nterferences. Those factors makes the smoke mage segmentaton relatvely complex. The mproved HIS mode s proposed to get smoke area, but the background color wll nfluence the smoke color judgment [2]. The fog mage s transformed by dual tree complex wavelet. The hdden Markov tree model s structured to express the smoke texture, and the smoke s detected by support vector machne. The dsadvantage s the hgh false rate [3]. Usng the characterstcs of wavelet transform spatal frequency, smoke texture enhancement mage s acheved by weghted average processng of the mage after wavelet transform coeffcents decomposton nto multlayer. Then, usng the dfferental box countng method to calculate the numercal traversng of the mage, to get each pxel fractal dmenson, smoke segmentaton mage s obtaned by threshold method. When the texture nformaton of an mage s not rch, ths method can easly produce the over segmentaton [4]. Sngle Gauss background dfference method s presented to obtan the foreground object. Because the dfference threshold s fxed, t cannot meet the demand of automatc fre smoke detecton [5]. *Address correspondence to ths author at the School of Informaton and Control Engneerng, X an Unversty of Archtecture and Technology, X an, Shaanx, , Chna; Tel: ; E-mal: huyan.nancy@163.com In the early fre smoke mage detecton, the dfference n smoke concentraton leads to dfferent extents of shadng for the background. So, usng the characterstcs of early smoke, blockng technology wll be appled to mage segmentaton to mprove smoke mage segmentaton effect. And the method has a certan practcalty. 2. SMOKE IMAGE SEGMENTATION MODEL Movng target detecton technology [6] extracts foreground object through analyzng mage moton nformaton. Background dfference method and frame dfference method are good real-tme and favored by smple calculaton. Because of unque characterstcs of smoke mage, f these methods are drectly appled to the smoke mage segmentaton, there exst the followng problems: 1) Owng to smoke mage color non-unform, the background dfference method s senstve to llumnaton changes. That fxed dfference threshold s too large or too small s prone to over segmentaton or leakage segmentaton. 2) Smoke mage moton s slow n consecutve frames. Frame dfference method produces larger vods. Extracton of the target s not complete. Smoke can be dvded nto smolderng smoke and fre smoke [7]. Early fre recognton focuses on the smolderng smoke. Smolderng smoke mage color s gven prorty to whte, grey and whte. Smoke keeps churnng n the heat. The smoke concentraton near the fre pont s greater than the edge. Adoptng the block technology, the dfferent blocks wth dfferent dfference threshold, when suspected smoke number of pxels n block meets the preset condton, the block s smoke area, otherwse regarded as dsturbance. The specfc steps are as follows: / Bentham Open

2 28 The Open Constructon and Buldng Technology Journal, 2015, Volume 9 Yan et al. Step 1: Block segmentaton. I t s the current frame at t moment. B t (x, s the background frame. The two frames sze s M! N. Every block sze of the frame s L! L. Extracton of suspcous targets n block unts not only can mprove the effcency of detecton, but also reduce nose nterference. s a cyclc varable, whch represents the number of mage blocks. Each block s handled as follows: It,, B, D and T t, respectvely express the current frame, background frame, front two dfference mage, and block dfference threshold for block of (x, pxel at the t tme. Num s pxel s number of satsfyng (1) n the block. If Num s greater than 0.75! ( L! L ), the block s consdered suspcous smoke object. If the pxels of ths object are ntercalated, t s 1, otherwse 0. Step 2: Judgment of connected doman blocks [8]. Smoke s a slow process of change from appearance to perdton. When a suspcous object area s larger than a certan threshold, t s probably because mutaton caused by llumnaton leads to the area to relatvely large, even close to full frame mage. In addton, a lttle suspcous objects may also be extracted by the effects of nose. So, the connected doman area s counted whether or not ths s smoke. = " D (2) (x, y )!R j s a parameter to express the area of connected doman R j ( j = 1, 2,..., m). When the parameter / ( M! N ) s greater than 4% and less than 60%, R j s smoke. Step 3: Processng of morphology [9]. The use of open operaton can elmnate scatter and burr smooth effect on the mage. Closed operaton can fll the vod, by brdgng the crack. The two-mage processng cannot change the locaton and shape of the orgnal mage. They are defned as follows: X!S = ( X!S) " S (3) X S = ( X! S)"S (4) Step 4: Dfferental threshold updatng. The dfferental threshold of movng object detecton based on background dfference could nfluence the effect of target detecton. Because of smoke reunon characterstc [10], the block based dfferental threshold updatng can guarantee wthn a certan tme nterval that the smoke can be accumulated wth enough dfferences and formed potentally suspcous target formaton. If the current block s detected as a suspcous object, t shows that ths block dfferental threshold s proper. Otherwse, t shows that ths block dfferental threshold s too large. It therefore, needs to be updated. Dfferental degree between current frame and background frame s used as the updatng reference. The mean dfferental threshold of all pxels s used as the block fnal dfferental threshold. (1) $ 1! # T + (1 %!) I % Bt,, L # ) L " L# L Tt + 1, = ' f b l o c k = 0 T, f b l o c kt, = 1 ( α s a scale factor, whch generally takes Step 5: Background mage updatng. In practcal stuatons, appearance of fre smoke crcumstances are less. In order to mprove the detecton speed of the algorthm, the background mage updatng s dvded nto global updatng and local updatng. If the current frame s not extracted nto suspcous objects, background mage s globally updated wth the current frame. But thanks to the exstence of nose, a small amount of suspcous objects could nevtably be extracted. Therefore, when the number of pxels n the suspcous object s less than the threshold, they are not smoke objects, and the current frame s used as a new background mage. The emergence of fre smoke s slow growth change process. It may not start to occupy most of the regonal mage. So, when the number of suspcous object pxel s over the mage sze of 70%, ths s probably because of sudden llumnaton changes, such as swtchng lghts, led to the abnormal foreground extracton, then to the background of global updatng. Followng the above stuaton, the background mage s locally updated. B t + 1, (5)! $ ' B + (1 % ') I, f block = 0 = (6) #!" Bt,, f block t, = 1 block s block pxel coordnate value at t tme. Smoke mage segmentaton algorthm flow chart s shown n Fg. (1). Two-smoke mages are segmented as shown n Fg. (2). by usng Fg. (1). respectvely. The proposed algorthm can extract more complete smoke foreground target, mantan the orgnal smoke mage rregular edge nformaton, and elmnate the nterference of smlar color wth smoke, see Fg. (2b). The tme of segmentaton algorthm s less than 1.5s, meetng the real-tme requrements of the system. But, updatng background mage stll retans a small smoke around the target, see Fg. (2c). When ths background mage s used as the new background mage agan to get foreground object, t s easer to cause that the target edge nformaton s not complete. 3. CORRECTION OF THE BACKGROUND IMAGE UPDATING Because of the characterstc of the smoke concentraton that t gradually becomes weaker from the nteror to the edge, the color n the edge s dark smlar to the background. The updatng background mage stll retans a small smoke around the target, see Fg. (2c) by formula (6). It s very unfavorable for follow-up of the suspcous targets detecton.

3 Early Fre Smoke Image Segmentaton The Open Constructon and Buldng Technology Journal, 2015, Volume 9 29 Fg. (1). Flow chart of smoke mage segmentaton algorthm. So, the formula (6) s modfed by the orgnal background mage to get equaton 7. The smulaton result s shown n Fg. (3). B t+1, ' =! " B t, + (1#! ) B t+1, (7) It can be seen that there are traces of small smoke around the target as shown n Fg. (2c). They are smoothed well by usng formula (7) so that the new background mage s more close to the true background. 4. SIMULATION EXPERIMENTS AND RESULTS ANALYSIS In order to verfy the performance of the segmentaton algorthm, four vdeos are examned respectvely. A part of vdeos are shot by authors research group ncludng smoke and nterference vdeos. Another part of vdeos are from The segmentaton results are shown n Fg. (4).

4 30 The Open Constructon and Buldng Technology Journal, 2015, Volume 9 Yan et al. (a) Orgnal mages (b) Proposed algorthm (c) Orgnal background and updated background mage Fg. (2). Smoke mage segmentaton and background updatng based on block. Fg. (3). Correcton for Fg. (2c) (! = 0.7). Fg. (4). Smoke mages and the segmentaton results. (a) Smoke mages (b) Proposed algorthm (c) The background dfference method

5 Early Fre Smoke Image Segmentaton The Open Constructon and Buldng Technology Journal, 2015, Volume 9 31 Compared wth the background dfference method, the proposed algorthm can extract more complete smoke target, and elmnate nterferences, for example pedestran, complex background, lght and reflected lght. However, ndoor smoke mage segmentaton effect of background dfference method s better than that of outdoor. Ths s manly because of nterferences, such as lght changes, reflectng lght n outdoor complex space. Those nterferences wll result n the excessve object segmentaton regon, whch wll affect the accuracy of target recognton. CONCLUSION Image segmentaton s the premse and foundaton of pattern recognton. It can reduce the amount of data to be processed, and enhance the real-tme of the algorthm. At present, the segmentaton method of fre smoke mage commonly use fxed threshold method whch s prone to over segmentaton or leakage segmentaton. And ths segmentaton method gave ncomplete nformaton of the edge, whch s not conducve to the suspected target feature extracton. Therefore, smoke mage segmentaton algorthm based on blockng and adaptve background updatng s proposed. The method uses mage characterstcs n the spatal doman changes. Dfferent blocks wth dfferent dfference thresholds can extract more complete suspected smoke target. And real-tme dfferental threshold s updated. The background mage updated by weghtng s revsed usng the orgnal background mage to ensure that the new background mage can be an even more true reflecton of the scene change. Smulaton results show that ths method can extract most of the suspected smoke mage n a complex space, the edge nformaton s relatvely complete, and s strongly nterference resstant. CONFLICT OF INTEREST The authors confrm that ths artcle content has no conflct of nterest. ACKNOWLEDGEMENTS Sncere thanks for the teacher Wang for hs careful gudance n the practce of scentfc research and paper wrtng. At the same tme, I would also lke to thank the members of the research team for ther help and support. Foundaton: The specal scentfc research plan project of Shaanx Provnce Educaton Offce (14JK1438), and The major scence and Technology achevement nnovaton fund of X an Unversty of Archtecture and Technology (ZC1103) are acknowledged. REFERENCES [1] T. Chen, Y. Yn, S. Huang, and Y. T. Ye, The smoke detecton for early fre alarmng system based on vdeo processng, IIH- MSP'06, Pasadena, Calforna, USA, 2006, pp [2] D. Krstnc, D. Stpancev, and T. Jakovcevc, Hstogram-based smoke segmentaton n forest fre detecton system, Informaton Technology and Control, vol. 38, pp , [3] R. J. Ferrar, H. Zhang, and C. R. Kube, Real-tme detecton of steam n vdeo mage, Pattern Recognton, vol. 40, pp , [4] Y. Huang, and Y. Lv, Early fre smoke mage segmentaton based on enhancement of frequency feld and dfferental box-countng fractal dmenson, Laser Journal, vol. 35, pp , [5] J. Yang, F. Chen, and W. Zhang, Vsual-based smoke detecton usng support vector machne, ICNC, Jnan, Chna, 2008, pp [6] D. Han, and B. Lee, Flame and smoke detecton method for earlytme detecton of a tunnel fre, Fre Safety Journal, vol. 44, pp , [7] C. Yu, J. Fang, J. Wang, and Y. Zhang, Vdeo fre smoke detecton usng moton and color features, Fre Technology, vol. 46, pp , [8] F. Zhao, Z. Lu, Z. Zhang, and H. Lu, A hardware acceleraton based algorthm for real-tme bnary mage connected-component labelng, Journal of Electroncs Informaton Technology, vol. 33, pp , [9] M. M. Thouraya, B. Malka, and B. Bachr, Mathematcal morphology for TOFD mage analyss and automatc crack detecton, Ultrasoncs, vol. 54, pp , [10] A. Rafee, R. Tavakol, R. Danat, and S. Abbaspour, Fre and smoke detecton usng wavelet analyss and dsorder characterstcs, ICCRD 3 rd Internatonal Conference on Computer Research and Development, vol. 3, 2011, pp Receved: December 17, 2014 Revsed: January 25, 2015 Accepted: May 14, 2015 Yan et al.; Lcensee Bentham Open. Ths s an open access artcle lcensed under the terms of the Creatve Commons Attrbuton Non-Commercal Lcense ( lcenses/by-nc/4.0/) whch permts unrestrcted, non-commercal use, dstrbuton and reproducton n any medum, provded the work s properly cted.