Shadow Elimination of Moving Targets Base on a Novel Two-Step Algorithm

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1 Send Orders for Reprnts to The Open Cybernetcs & Systemcs Journal Open Access Shadow Elmnaton of Movng Targets Base on a Novel Two-Step Algorthm Lu Yng * Chongqng Jaotong Unversty Chongqng P.R. Chna Abstract: Ths paper presents a two-step method for elmnatng the shadows of movng targets. As the frst step we mprove the Gaussan mxture model for obtanng a real-tme background and a movng foreground; secondly we fgure out the ntersecton of background dfference and movng foreground for elmnatng the concomtant shadow [1-5]. Experment results ndcate that our method has a preferable performance on elmnatng the shadow n ts entrety. Keywords: Elmnaton gaussan mxture model movng targets shadow. 1. INTRODUCTION In nature a movng target carres ts own shadow owng to the llumnaton of lght (ether artfcal or from the sun) n crcumstances. The shadow sze s decded on the relatve poston of lght source and the target. As the shadow area s large at a certan tme t wll drectly affect the extracton accuracy of a movng target and cause dstorton. The Gaussan mxture model proposed by Grmson and Stauffer s one of the most wdely used tools for movng object extracton but not so effectve n processng the shadow of a movng target [6-8]. How the Gaussan mxture model for target extracton s capable of dealng wth shadow s gven n Table 1. As we can see obvous shadows n the 340th and 398th frames are detrmental for the extracton of movng objects [4]. 2. IMPROVED MODEL FOR SHADOW REDUCING Frstly we attempt to obtan a real-tme background and a movng foreground by mprovng the Gaussan mxture model and then work out the ntersecton of background dfference and movng foreground so as to reduce the nfluence of the target shadow on the result of test. The specfc establshment of our model and updated algorthm can be descrbed as follows: For mage I we represent x as pxel and t as tme we smulate the process of each pxel x usng K models M. For each model assumng ts mean value s μ ts weght s ts survval tme s l and the frequency of occurrence s f. To ntalze the Gauss model we use the frst mage frame to buld a model for each pxel. In the frst place parameter μ s set as the value of pxel whch means color or gray level the weght value s 1 survval tme l s 175 and the frequency of occurrence f s 0. Then we use the new pxel values Pt ( x y ) to match the K th Gauss dstrbuton. If formula 1 s satsfed. Pt( x y) μ ( x y) Th (1) We reckon Pt ( x y) as matchng th Gauss dstrbuton. Heren Tht means tme and matchng threshold of the th Gauss model s determned by formula 2. ( max( ( )) mn( ( )) Th = P x y P x y DIV t t t There n DIV s the proportonal coeffcent. The greater the DIV the lower the threshold and the hgher the resoluton of model. Matchng threshold of the model s proportonal to the color change of current frame. max(p t (x y) s the maxmum value of pxel n the frame of the tme whle mn( Pt ( x y ) s the mnmum. Because of nfluence from real envronmen nose s nevtable and brghtness and color of the vdeo frames are also n constant change. If a fxed threshold s employed n the method the system resoluton wll be reduced when there are obvous changes n the mage. Assocatng the threshold value wth the current vdeo frame [8 9] we can ensure that the resoluton of the system s also mantaned at the same level. Therefore settng of the matchng threshold s reasonable. In the ntalzaton phase of the Gauss model f the pxel P t (x y) of the tme t matches the th model then we update the model parameter as follows: ( xy) ( xy) t+ 1 = + 1 ( ) ( xy ) ( xy ) sgn P( xy ) ( xy ) μ + = μ + μ t 1 t (2) (3) (4) X/ Bentham Open

2 762 The Open Cybernetcs & Systemcs Journal 2015 Volume 9 Lu Yng Table 1. Results of Gaussan mxture model for real vdeo processng (1-1). Frames Current Frame Gauss Mxture Model ( ) ( ) ( ) = ( ) ft+ 1 x y = f x y + 1 l ft+ 1 x y f x y l ( x y) lt+ 1 = 255 t t ( x y) ( x y) 255 = 255 For the unmatched model the parameters are updated as below: μ ( xy ) ( xy ) t+ 1 = ( xy ) μ ( xy ) t+ 1 = ( ) ( ) f x y f x y t+ 1 = lt+ 1 ( x y) = l ( x y) 1 (5) (6) (7) (8) (9) (10) There n ( xy) μ ( xy) f ( x y ) l ( x y ) t t t t are the wegh mean frequency and survval tme of the pxels Pxy ( ) n the t tme and the th model respectvely. Sgn (number) s a symbol functon (Table 2): Table 2. Symbol functon (1-2). Number Sgn (number) >=0 1 =0 0 <=0-1

3 Shadow Elmnaton of Movng Targets Base on a Novel Two-Step Algorthm The Open Cybernetcs & Systemcs Journal 2015 Volume We devse a new method to update parameters when the ntalzaton process of the model s completed. The model wll qut when weght value and survval tme l of the model turn to be 0 and replace the orgnal model wth a new one. The parameter of the new model s set as the same as the vdeo frame. The mean value of model parameter μ s the value of pxel whch means color or gray level ts weght s 1 the survval tme l s set as 225 and the frequency of occurrence f s 0. Parameter update of the model matchng and nonmatchng s the same as formula 11 and 12 for not extng model. The only dfference s the way of updatng weght. If matchng: ( xy) ( xy) N( x) t+ 1 = + t 1 If not matchng: ( xy) ( xy) (11) t+ 1 = 1 (12) There n Nt () x s the quantty of the model at pxel Pxy ( ) n tme t. Durng buldng smulaton updatng and ext of the model the weght of pxel wll ncrease or decrease dependng on whether there exsts a matchng model. Weght wll ncrease when the pxel matches some Gauss model whle weght wll decrease when such a Gauss model does not exst. The dstrbuton of weght wll gradually focus on the background wth large matchng opportunty. The model mean value changes along wth the varaton of vdeo frame pxel when matchng makng the model adapt to the slow varaton of the background. The survval tme s l when the model matches. And when the model msmatches ts survval tme begns to count down and t won t tme untl the model completes ts matchng agan. Reproducng frequency of the model s accumulatve calculaton frequency of model matchng durng the survval tme of the model. The model wll ext when the survval tme of background model and recurrence frequency are zero [8-10]. Thus the background of short survval tme and hgh frequency do not ext only because of reducton of weght reducton. For example the small crculaton changes exst n the background such as the wnd leaves the flyng flags the rpplng waves on water and so on. Therefore ths knd of desgn of algorthm to a certan exten possesses a stronger ablty to tolerate a hgher frequency nose than Guess model. Thus t effectvely suppresses nose and can further approach as well as smulate the background. 3. THE EXPERIMENT AND ANALYSIS Frstly we buld the model for the background wth our mproved Gaussan mxture model. The process of ntalzng the vdeo background s gven n Table 3: Exhbt n Table 3 also shows that background elements of the background mage and the current frame are consstent when the n s 175. Thus the vdeo montorng system obtan a trusted background by mprovng Gaussan mxture model. Extracton of foreground n the background modelng by mproved the Gauss mxture model and precson of new background dfference module s guaranteed bases on the background. Table 3. Background ntalzaton (1-3). Frames Real tme (local) Images Correspondng Background In the Establshng Process 2 86 (Table 3) Contd.

4 764 The Open Cybernetcs & Systemcs Journal 2015 Volume 9 Lu Yng Frames Real tme (local) Images Correspondng Background In the Establshng Process The Method wth an Improved Verson on Gauss Mxture Model The performance wth an mproved Gauss mxture model buldng model s shown n Table 4: In terms of result contras though the extracted movng object s relable the result s stll not so deal. Ths s because the nosy components of the effectve pxel are stll exstent n extracton results of whch the bggest s orgnated from the shadow of a movng object. The sze of target shadow s uncertan snce the poston of lght source s undetermned n smulaton. The nfluence of the shadow s relatvely nsgnfcant when the lght source s rght above the target or the angle tlts slghtly. However the lght source such as that from the sun wll change from tme to tme. Or even a fxed lght source s relatve to the target locaton accordng to ts movement. The sze and length of the shadow s nversely proportonal to the dstance of the relatve poston. The above expermental analyss mples that the operaton of mproved Gauss mxed background model has already provded a stable model for the system whle the key to the accuracy of the background dfference depends on the precson and real-tme performance of background mage. Therefore ncreasng the background dfference module on the bass of mxed Gauss background model s feasble The Method of Increasng Background Dfference Module n Gauss Mxture When background dfference ncreases the real operaton results of the experment are shown n Table 5. As s shown n Table 5 the nhbtory effect of ths algorthm on the shadow s obvous. The result nherts the advantages of both Gaussan mxture background model and background dfference. Our mproved algorthm can effectvely overcome the shadow and obvously nhbt any shadow of a movng target The Contrast of the Algorthm and Background Dfference Method If some of the vdeo s obscure choose a real survellance vdeo for contrast of the results of the algorthm and those of the background dfference method: The background dfference method has an obvous nhbtng effect on the target shadow and t can be analyzed from Table 6 that the effect of nose suppresson of the algorthm adopted for ths paper s far better than the background dfference method and the nhbtng effect of a target shadow s more deal than the background dfference method [10-13] gvng users more convenence n the process of observaton and analyss. Fg. (1). Contrast of target pxels numbers under the real survellance vdeo. It can be analyzed from (Fg. 1) that the pxel numbers of the algorthm are better than those of the background dfference method. Under a hgh resoluton the advantages of ths algorthm can be better reflected.

5 Shadow Elmnaton of Movng Targets Base on a Novel Two-Step Algorthm The Open Cybernetcs & Systemcs Journal 2015 Volume Table 4. Performance of mproved Gauss mxture model (1-4). Frames Current Frame Background Frame Extracton Frame of Movng Targets

6 766 The Open Cybernetcs & Systemcs Journal 2015 Volume 9 Lu Yng Table 5. Extracton of movng target wth new algorthm (1-5). Frames Current Frame Foreground Frame

7 Shadow Elmnaton of Movng Targets Base on a Novel Two-Step Algorthm The Open Cybernetcs & Systemcs Journal 2015 Volume Table 6. Contrast of the algorthm and background dfference method uuder the real survellance vdeo (1-6). Frames Current Frames Algorthm Background Dfference Method The Contrast of Algorthm s Date wth Classc Mxture of Gaussan Background Model In movng target detecton the modelng algorthm of classc Gaussan mxture background often occurs. In the followng we wll compare wth ths paper s the modelng algorthm of the combnaton of background dfference method and mproved Gaussan mxture background. In the comparson the vdeo s self recorded to smulate the workng condton of outdoor envronment s vdeo nput devces. As shown n Table 7 the accuracy of the msdetecton rate of two algorthms s not hgh. Besdes the classc Gaussan mxture background model on vdeo processng s not fluent and more frequent. As shown n Table 8 the speed of processng vdeo of the modelng of the combnaton of background dfference method and modfed Gaussan mxture background s almost the same but faster than the modelng of classc Gaussan mxture background.

8 768 The Open Cybernetcs & Systemcs Journal 2015 Volume 9 Lu Yng Table 7. Mss rate (1-7). Name of Algorthm Vdeo Frame of Experment Msdetecton rate Percentage Modelng of classc Gaussan mxture background % The algorthm % Table 8. Tme of processng vdeo (1-8). Name of Algorthm Vdeo Frame of Experment Rme of Processng Vdeo Modelng of classc Gaussan mxture background :05:28:592 Algorthm :05:26:249 Table 9. Background rebult tmes (1-9) [14]. Name of Algorthm Vdeo Frame of Experment Background Change Tmes Background Rebult Tmes Bmodelng of classc Gaussan mxture background (100%) Balgorthm (37.5%) As shown n Table 9 External factors as wnd and pavement vbraton (as shown n the experment) have caused camera shake (a slde of the vdeo equpment due to a larger shake) and the background has changed 8 tmes. In these changes the classc Gaussan algorthm has rebult ther background. Ths wde range smulaton drectly leadng to the detecton of movng targets and dsappearng n the wndow affects the normal work of system. But the algorthms adopted by ths paper smulated background only 3 tmes and on a small scale n the 8 background changes and n all the 3 tmes smulatons users Observaton and use are not so obvously affected. Although at some pont n tme or wthn a tme slce wndows contanng the amount of nformaton are slghtly more the observaton of movng targets s no problem [13]. Ths algorthm can be better adapted to the envronmen whch means t s hghly robust. CONCLUSION In the detecton of movng targets t s suggested that the classcal mxture Gaussan background modelng algorthm be used more. Although the Gaussan mxture model s partly able to elmnate the shadow yet f the shadow area s larger t wll drectly affect the extracton performance of movng objects. Through the processng of mprovemen the Gaussan mxture modelng can obvously reduce the shadow area. But the algorthm of combned mproved Gaussan mxture model s more advantageous than that of the background dfference method n the present paper as t can completely elmnate the shadow. So the algorthm s better and t has more advantages than Gaussan algorthm n terms of mss rate [15] processng tme and numbers of background reconstructon. Therefore t can better detect movng targets. CONFLICT OF INTEREST The author confrms that ths artcle content has no conflct of nterest. ACKNOWLEDGEMENTS The paper s founded by the Natonal Natural Scence Foundaton Project ( ) and the Open Fund Project of Mnstry of Educaton (SLK2014B05). REFERENCES [1] L..Q. Shang-bng and Lnlng Detecton method of movng objects base on background dfference and frame dfference Chnese Journal of scentfc nstrumen Shangha Chna [2] Z. Zvkovc Improved adaptve Gaussan mxture model for background substracton In: Proceedngs of the Internatonal Conference on Pattern Recognton Amsterdam Netherlands [3] P. KaewTraKulPong and R. Bowden An Improved Adaptve Background Mxture Model for Reahne Trackng wth Shadow Detecton Kluwer Academc Publshers Kngston UK [4] D. S. Lee Improved adaptve mxture learnng for robust vdeo background modelng In: Proceedng of IAPR Workshop on Machne Vson for Applcaton Nara Japan [5] L. Lu and Yun-hu Adaptve algorthm of multmodal fast background dfference Chna Journal of Image and Graphcs Henan Chna vol. 32 no. 5 pp [6] Lujng and Wanglng An mproved algorthm of Gaussan mxture model Computer Engneerng and Applcatons Hebe Chna vol. 47 no. 18 pp [7] C. Stauffer and W. Crmson Learnng patterns of actvty usng realtme trackng IEEE Transactons on Pattern Analyss and Machne Intellgence vol. 22 no. 8 pp Aug [8] R. Y. Mao and X. N. Chen The mproved HSI space morphology have nose of color mage edge detecton Computer Applcaton Research vol. 42 no. 24 pp Feb [9] J. Lu and L. Wang An mproved algorthm of the gaussan mxture model Background method Computer Engneerng and Applcaton vol. 52 no. 30 pp

9 Shadow Elmnaton of Movng Targets Base on a Novel Two-Step Algorthm The Open Cybernetcs & Systemcs Journal 2015 Volume [10] Y.Y. Wan Robustness of the morphology of color mage edge detecton algorthm Computer Engneerng and Applcaton vol. 54 no. 21 pp [11] X. J. Huang J.M. Zhou and B.Y. Lu Adaptve mxture gaussan background model of movng target detecton method Computer Applcaton vol. 37 no. 24 pp [12] L. Wang and N.H.C. Yung Extracton of movng objects from ther background based on multple adaptve thresholds and boundary evaluaton Intellgent Transportaton Systems vol. 45 no. 20 pp [13] G.Q. Luo Movng Target Detecton and Trackng n Intellgent Vdeo Survellance Master Thess Guangdong Unversty of Technology Guangdong of Chna [14] J.J. Duan Based on Color Space Power Battery Weldng Pont Postonng Technology Research Master's Thess North central Unversty [15] C. Ma F. de Jun and Z. L Combned wth threshold segmentaton and morphologcal buldng shadow detecton Surveyng and Mappng vol. 26 no. 11 pp Receved: September Revsed: December Accepted: December Lu Yng; Lcensee Bentham Open. Ths s an open access artcle lcensed under the terms of the Creatve Commons Attrbuton Non-Commercal Lcense ( whch permts unrestrcted non-commercal use dstrbuton and reproducton n any medum provded the work s properly cted.