OBJECT TRACKING BY ADAPTIVE MEAN SHIFT WITH KERNEL BASED CENTROID METHOD

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1 ISSN : Vol. 3, No., Janary-Jne 202, pp OBJECT TRACKING BY ADAPTIVE MEAN SHIFT WITH KERNEL BASED CENTROID METHOD Rahl Mshra, Mahesh K. Chohan 2, and Dhraj Ntnawwre 3,2,3 Department of Electroncs, IET DAVV, Indore (M.P.) E-mal: rmshra_sat@yahoo.com, 3 dhrajntnawwre@gmal.com ABSTRACT An approach towards the object trackng, the mpactfl method object mprove localzaton, s proposed. Object s frstly optmze by the adaptve mean shft, f there s hgh localzaton then crrent frame of vdeo replace by the next frame and adaptve mean shft s se to maxmze the Bhattacharya coeffcent.if there s low localzaton by adaptve mean shft then hstogram based target representaton s reglarzed by an sotropc kernel and then centrod s calclated by the bnary gradent matrx. Ths means we add some strctre nformaton n featre space. Ths method s sccessflly coped wth fast movng object, camera vdeo, target scale change and partally occlsons. The proposed method s very effcent n case of both comptatonal and space crtera. Keywords: Trackng,Localzaton,Target representaton,bhattacharya coeffcent. INTRODUCTION Becase of the advance technology, there are many applcatons n feld of object trackng. It s wdely appled n the feld of robotc control, traffc srvellance, smart rooms, drver assstance and many homng technologes. Many object trackng algorthm generated n lteratre, and among them kernel based object trackng s most effcent and robst.comanc n [,2] sed frst tme kernel based mean shft to track movng object. He sed Bhattacharya coeffcent to relate the target model and target canddate and sed mean shft algorthm to track and search the target canddate and optmze t. Ths method s sccessfl to track object n many applcaton and after hs research many object trackng researcher sed ths algorthm as a base n ther modfed algorthms. Peng [3] sed pdated model of mean shft algorthm to track object. In[3], peng proposed the method n whch atomatc selecton of bandwdth of kernel s selected. For many years mean shft remans the base of the object trackng. However mean shft has many drawbacks lke backgrond nose, localzaton error, object loss etc. When object moves otsde the trackng wndow mean shft algorthm s nable to track target canddate at bondary of the trackng wndow becase mean centre of the wndow s nable to move wth target canddate. In ths case object wll be loss and trackng error occrs. Localzaton error occrs becase of the backgrond pxels, these backgrond pxels certanly ntrodced de to target model whch contans target canddate. To redce localzaton error backgrond pxels s removed from the target model when the hstogram s compted. Collns [4] sed approach n whch he samples the pxels of the target model contans target canddate and made a crclar centred rng of pxels to omt the backgrond pxels and create color hstogram then mean shft s sed to track the object. Ylmaz [5] sed asymmetrc kernel mean shft wth atomatc scale and orentaton n whch mean shft algorthm s performed by translatng a kernel n featre space n sch a manner that object observaton n crrent and past frame are smlar. Ths approach solved the problem of change of scale and orentaton of object frame by frame, t extend the tradtonal mean shft. Bt ths method s also sffers from the constancy of kernel shape becase of contor trackng. All above method sed color and grey level hstograms as base of kernel based object trackng and work effcently for many applcaton. Stll n many complex object trackng algorthm color and grey level hstograms s not sffcent alone becase t create problem when smlar color objects occrs n sngle frame. In[6], To make tradtonal mean shft more effcent wth kernel based object trackng some strctre featre s appled wth hstogram featre. In [6] edge based centrod tends to converge the target orgnal centre and determne object strctre. In proposed method we sed adaptve mean shft to track the object frame by frame even when ts scale and orentaton of the target changes. When object movng fast and tend to move otsde the target model wndow tradtonal adaptve mean shft s modfed. We modfyng t by add strctre nformaton n tradtonal method to locate and optmze the target tre centrod. Ths report s organzed as follows: In Secton II, We gve overvew of tradtonal mean shft. In Secton III asymmetrc kernel based object trackng s descrbed. In Secton IV, adaptve mean shft wth kernel base centrod

2 40 method s descrbed. In Secton V, comparatve reslts of both methods are gven. In Secton VI contans Conclson and ftre work. 2. MEAN SHIFT ALGORITHM Mean shft algorthm s very effcent and robst technqe n object trackng applcaton. It s broadly classfed n two components: target representaton and target localzaton. It s orgnally developed by Fknaga and Hostetler [7]. 2.. Target Representaton In object trackng algorthms target representaton s manly rectanglar or ellptcal regon. It contan target model and target canddate. To characterze the target color hstogram s choosen. Target model s generally represented by ts probablty densty fncton (pdf). Target model s reglarzed by spatal maskng wth an asymmetrc kernel.pdf s calclated throgh kernel densty estmaton. n x x f(x) = K d nh h Here h s the bandwdth of the kernel, n s the total nmber of pont n the kernel.in target representaton target s selected manally n frst frame and the pdf of the second frame s calclated throgh pˆ() y = n 2 y x Ch K δ[ b() x ] h Here Ch = 2 n y x K h C h s the normalzaton constant. It s not depend on the vale of y and can be calclated earler for gven kernel and dfferent vale of h.h defnes the scale of the target canddate. The presence of the contnos kernel ntrodces nterpolaton process between locaton and mage Bhattacharya Coeffcent Ths smlarty fncton defne the correlaton between target model and target canddate. It generally gves the dstance between target model and target canddate, the dstance shold have metrc strctre. It s specfed n the form of dstance between two dscrete dstrbton gven by Where pˆ() y = d = () δ y (3) m pˆ [(), pˆ y](), qˆ = pˆ y qˆ (4) = Where pˆ() y s referred as Bhattacharya coeffcent. New target locaton y n crrent frame s fond by IJCSC teratvely proceedng towards the maxma n neghborhood. y Is fond by travellng throgh ts ntal poston y 0 t s gven by y = nh x y nh w Where are representatve weghts. 3. ADAPTIVE KERNEL BASED OBJECT TRACKING The scale of the target often changes n tme so bandwdth of the kernel has to be adapt accordng to target sze. Ths s possble de to scale nvarance property of the adaptve mean shft algorthm. As target can enlarge or shrnk gradally n consectve frame, no trackng algorthm can track object effectvely. For solvng ths problem adaptve kernel based mean shft s developed. In ths algorthm area of the target s estmate n prevos frames and n the crrent frame the area of the target canddate regon be a lttle bgger than the estmated area of the target. Therefor, f the scale and orentaton of the target change, target wll get lttle bgger area n the crrent frame then the prevos frame. Real estmaton of area done throgh the mean shft algorthm, the weght mage where the weght of the pxels s the sqare root of the rato of ts color hstogram of target model to ts color probablty n target canddate. The target s sally n the bg target canddate regon. De to the exstence of the backgrond featres n the target canddate regon, the probablty of the target featres s less than that n the target model. Ths method s more relable then CAMSHIFT becase of ts better estmaton reslt. The man advantage of the adaptve kernel based trackng s that the more non object regon resdes otsde the kernel. 4. Kernel Based Adaptve Mean Shft wth Centrod Estmaton Tradtonal kernel based mean shft s bascally rely on the spectral featres of mage, whch create problem when the same color objects comes n same frame and prodce localzaton error. To avod ths error some strctre nformaton has to be add wth spectral featres whch combne effect of both color and grey level featre. Basc mean shft rely on many thngs lke srrondng, sze, shape and when objects goes otsde of the target wndow tracker wll fal to estmate the poston of the objects. In the proposed algorthm kernel based adaptve mean shft s sed to track the object when t prodce localzaton error. When object movng n dfferent drecton and changng ts sze and shape contnosly and also tends to go otsde the target wndow, wndow wll adjst ts sze by takng ts centre to the new poston. We sed adaptve wndow sze to maxmze the Bhattacharya coeffcent. (5)

3 Object Trackng by Adaptve Mean Shft wth Kernel Based Centrod Method 4 Followng steps are taken n the proposed algorthm to track object:. In frst frame target s selected manally n the target wndow whose centre s proposed by the mean shft and applcaton of kernel based object trackng s appled. 2. If the kernel based mean shft s workng properly then we pt the adaptve wndow sze and maxmze the Bhattacharya coeffcent. It means hgh localzaton s acheved. 3. If the kernel based trackng s not workng properly then low localzaton s achved. Low localzaton means object s gong otsde the target wndow and wndow s nable to shft ts centre wth t. In ths scenaro we add strctre nformaton wth ts spectral featre and calclate the centrod by bnary graded matrx (BGM) at crrent poston n the wndow. 4. In case of worst localzaton, t means f occlson occrred then both step 2 and 3 wll not work properly and crrent frame changes to the next frame and repeat algorthm from step. The complete algorthm flow chart s shown n Fg. coordnate frst calclate wth adaptve kernel based mean shft and then ther corrected vale s fnd by or method. Fgre : Flow Chart for Proposed Algorthm 5. EXPERIMENTAL RESULTS Both kernel based adaptve mean shft and kernel based adaptve mean shft wth centrod estmaton are performed on dfferent vdeo fles and reslts fonds s satsfactory. In t we fnd that proposed method has less localzaton error then past one. Both x and y drecton Fgre 2(a): It Gves Coordnate Vale by Kernel Based Adaptve Mean Shft. (b) It Gves Corrected Coordnate Vale by Proposed Method. (c) It Gves Dfference Coordnate Vale by Two method

4 42 Table Comparatve Reslts Comparson Adaptve kernel Adeptve kernel of trackng based mean shft based mean shft wth centrod estmaton Coordnate 84(orgnal) 72(corrected) vale 2 Bhattacharya coeffcent 3 Nmber of teratons 6. CONCLUSION AND FUTURE WORK In proposed method problem weak localzaton wth changng of object shape and sze, partal occlsons s solved. Ths method s more effcent and robst as comparson to orgnal adaptve mean shft n many applcatons lke vsal srvellance, hman compter nteracton, traffc montorng, vechcle navgaton etc. Ftre work s done towards the enlargng the capabltes of trackers by makng t more robst to track object n more nosy condton lke fll occlson, changng lght condton complex object moton etc.the mltple object trackng n sngle frame wll also be proposed by ths technqe. REFERENCES [] Comanc, D., Ramesh, V. and Meer, P: (2003), Kernel- Based Object Trackng, IEEE Trans. Pattern Analyss Machne Intell., 25, No. 5, pp , [2] R. Collns., Mean-Shft Blob Trackng Throgh Scale Space. In IEEE Conf. on Compter Vson and Pattern Recognton, IJCSC [3] Ylmaz A., Javed O., Shah M., Object Trackng: a Srvey, ACM Comptng Srveys, 20(4), Artcle 3. [4] Comanc D., Meer P., Mean Shft: a Robst Approach Toward Featre Space Analyss, IEEE Trans Pattern Anal. Machne Intell., 2002, 24, (5), pp [5] Cheng Y., Mean Shft, Mode Seekng, and Clsterng, IEEE Trans on Pattern Anal. Machne Intell., 995, 7, (8), pp [6] Fknaga F., Hostetler L. D., The Estmaton of the Gradent of a Densty Fncton, wth Applcatons n Pattern Recognton, IEEE Trans. on Informaton Theory, 975, 2, (), pp [7] Dorn Comanc, Vsvanathan Ramesh and Peter Meer. Real Tme Trackng of Non Rgd Object Usng Mean Shft, IEEE Conf. On Compter Vson and Pattern Reog [8] Shlpa Wakode, Krthga A, K.K. Warhade, V.M. Wadha, Kernel Based Object Trackng wth Enhanced Localzaton, Sprnger 200. [9] D. Comanc and P. Meer., Mean Shft Analyss and Applcatons. In IEEE Int. Conf. on Compter Vson, 2, pp , 999. [0] F. Bashr, F. Porkl., Performance Evalaton of Object Detecton and Trackng Systems, IEEE Internatonal Workshop on Performance Evalaton of Trackng and Srvellance (PETS), Jne2009. [] A. Ylmaz, X. L, and M. Shah., Contor Based Objecttrackng wth Occlson Handlng n Vdeo Acqred Usng Moble Cameras. IEEE Trans. on Pattern Analyss and Machne Intellgence, 26(): pp , [2] Collns R.: Lectre on Mean Shft, [3] Yang C., Raman D., Davs L., Effcent Mean-Shft Trackng va a New Smlarty Measre, Proc. IEEE Conf. Compter Vson and Pattern Recognton, San Dego, CA, 2005,, pp

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