Target Tracking Analysis Based on Corner Registration Zhengxi Kang 1, a, Hui Zhao 1, b, Yuanzhen Dang 1, c

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1 Advanced Materals Research Onlne: ISSN: , Vols , pp do:0.408/ 03 Trans Tech Publcatons, Swtzerland Target Trackng Analyss Based on Corner Regstraton Zhengx Kang, a, Hu Zhao, b, Yuanzhen Dang, c The School of Informaton Scence and Engneerng, Shandong Unversty, Jnan, 5000, Chna a emal: @qq.com, b emal: hz@sdu.edu.cn, c emal: @qq.com Keywords: Mean-Shft; Corner Pont; Corner Regstraton Abstract. Target trackng algorthm based on Mean-Shft and Kalman flter does well n lnear trackng. However, the algorthm mght lose the target when the trace of moble target s curve or the acceleraton s not constant. To cope wth these drawbacks, ths paper proposes Target Trackng Analyss Based on Corner Regstraton. The algorthm modfes the ntal teraton center of Mean-Shft by usng the corner features combned wth affne transformaton theory and then the Mean-Shft can track the target. The theoretcal analyss and the expermental results demonstrate that ths method can overcome the drawbacks we talk above and make achevements n target trackng. Introducton Target trackng s based on mage processng technology. It ncludes pattern recognton, computer vson, and sports theory. Now there are many knds of vdeo trackng algorthm, such as Kalman flter [], Mean-Shft [], partcle flter algorthm [3]. Especally Mean-Shft algorthm because of ts robustness and applcable to dynamc background can be wdely appled. However, when trackng target s smlar to background gray or the speed of target s fast, Mean-Shft can't accurately track. When the target trajectory s nonlnear, Kalman trackng effect s not good. To solve these shortcomngs of two algorthms, n ths artcle proposes the Target Trackng Analyss Based on Corner Regstraton. Ths method modfes the ntal teraton center of Mean-Shft by usng the corner features and affne transformaton theory and then use Mean-Shft algorthm track the target. Mean-shft Algorthm A. Mean - Shft Algorthm n Trackng Applcatons Mean-Shft algorthm whch s appled n trackng target s based on kernel functon theory [4]. Frst of all establsh target model n the prevous frame [4]: n 0 qu = Ck( x x ) δ[ b( x) u] () = h For the next frame, due to the adjacent frame smlarty s large, so the ntal center n the next frame was the center of the prevous frame; the bandwdth s alsoh, the canddate model can be computed as: n 0 pu = Cyk x y δ[ b( y ) u] () = h Then compare smlarty of the two models by usng Bhattacharyya coeffcent [5]. Usng Taylor formula expands Bhattacharyya coeffcent: m n Ch p( y) p( y 0) q+ wk ( y x ) (3) u= = h And Mean-Shft vector s: All rghts reserved. No part of contents of ths paper may be reproduced or transmtted n any form or by any means wthout the wrtten permsson of Trans Tech Publcatons, (ID: , Pennsylvana State Unversty, Unversty Park, USA-8/05/6,00:6:4)

2 998 Optoelectroncs Engneerng and Informaton Technologes n Industry y= n = n = y 0 x xwg ( ) h y 0 x wg ( ) h ys the last arrve pont whch needs many teratons. Summary Mean-Shft algorthm steps: a) In the ntal vdeo frame, you need to select the target by usng the mouse and compute the target gray model. b) In the second frame and the follow-up frames, the center coordnate of prevous frame s the center coordnates of next frame, and also compute gray model of next frame. c) By usng Bhattacharyya smlarty, compare the smlarty of the targets whch one s n the prevous frame another one s n the next frame, f the smlarty s less than a threshold value, use (4) to teraton, and go to step b, untl more than a certan threshold. Mean-Shft algorthm only uses the pxel nformaton of target. It s not usng the movement nformaton. State matrx of Kalman just uses the target movement nformaton, so ths combnaton can track rapd lnear moton target. B. The Drawback of Mean-Shft and Kalman From Mean-Shft algorthm, t uses the prevous frame center as the ntal teratve center of the next frame. So f the target moves very quckly, the ntal center wll probably be located on background. So accordng to the drecton of Mean-Shft wll move to background[].although Kalman and Mean-Shft can track the fast movng target, but the target should move lnearly.because nput matrx and state-transton matrx are got by unformly accelerated moton equaton. So f the trace of target s curvlnear or no-constant acceleraton, the predcted value of Kalman wll also be located n background. So the ntal teratve center s very mportant n Mean-Shft algorthm. Improve Algorthm Based on Corner Regstraton A. Corner Extracton Ths paper uses Harrs corner detecton algorthm [6]. The basc prncple of the algorthm takes target pxels as the center of a small wndow, calculaton the gray level change of along any drectons n ths wdow. Determnng a pont whether a corner or not uses Harrs corner response functon: C( x, y) = det( M) k(trace( M)) (5) I I x xiy M = wx, y (6) IxIy Iy w s Gaussan template functon. det( M ) s determnant of matrx M. trace( M) s trace x, y of matrx. IxI y are the dfference of xand ydrecton. When the pont value C s greater than the gven threshold, t s the corner. B. Corner Regstraton Usng the relevant methods establsh a local matchng crtera. Support the prevous frame s A and the next frame s B. Smlarty between corner p whch s on the target of mage A and corner q n mage B uses correlaton coeffcent. It s defned as[7]: n n ρ ( pq, ) = [ Au ( + v, + j) µ ] [ Bu ( + v, + j) µ ] (7) n δδ π j= n= n µ µ and δδ are local mean and varance of the pontp and qof the mage A and B, n s radus of corner neghborhood. In order to reduce the amount of calculaton, so just match the corners whch are on the target. Takng square doman of the mage B whose center s pand the sde of square doman s d s the range to search the coarse corner ofp. Calculate the smlarty of p and q whch locates n square doman. (4)

3 Advanced Materals Research Vols phas more than one regstraton pont qwhch s n mage B, they should be fne matchng. The fne matchng use ther locaton nformaton [7].The algorthm s as follows: N( p ) spcorner pont support range whose center s p, the sde of square doman s m. To all the support corners ( ph, qr) whch phs n N( p ) and q r s n N( q j ), calculate ther support strength [7]: ρ( p, qj) ρ( ph, qr) φ( p, qj; ph, qr) Sup( p, qj; ph, qr) = (8) + dst( p, q ; p, q ) j h r Consder symmetry Sup( p, q ; p, q ) = Sup( q, p; q, p ).So the fnal matchng strength s [9]: j h r pr N( p), qh N( q) j r h S( p, q ) = Sup( p, q ; p, q ) (9) j j r h The ( p, q j),j=,,... whch are got by usng coarse matchng has many pars. If Sp (, qk) s bgger than other S( p, q j), j k, ( p, qk) s the best matchng pont. C. Improve Canddate Frame Intal Iteratve Center The mprovement s as follows: f the corner of prevous frame arex=, n,the target center pont sx 0, the regstraton corner of next frame are y=, n. Because two adjacent frames movement process can be consdered lnear moton, the matchng corner can be thought parallel affne. As long as get three groups whch are not collnear, then usng the affne transformaton theory can get mproved teratve center of canddate frame: aa b ycenter = 0 aa x b (0) Coeffcent matrx can be got from three non- collnear corners by usng the ternary equatons. In order to reduce calculaton, we should do pretreatment to pars of regstraton corners. Let vectoral coordnates do subtracton (): k N = ( q p ) q p () average k n = q j n s n mage B, n jn n jn n p s n mage A and they are got by fne matchng. Moton of adjacent frame can be thought lnear. So the subtracton qj p n s close to N n average.so select three pars of regstraton corners whch are the closest to N average Because the center x 0 s on the target, so by usng (0), y center s also on the target. Then we can use Mean-Shft track the target whch s curvlnear or no-constant acceleraton. D. Adaptve the Target Scale h= h prew,h the bandwdth of prevous frame, then choose two knds of bandwdth, h= hprew+ handh= hprew h, h= 0.h prew, then teraton Mean-Shft algorthm and compare Bhattacharyya coeffcent, bandwdth whch has the bggest Bhattacharyya coeffcent denotes h opt, to avod oversenstve scale adapton, the bandwdth assocated wth the current frame s obtaned through flterng[4]: h = γh + ( γ) h, γ = 0. () new opt prev Summary algorthm steps talked above: a) Choose the trackng target of the ntal frame and calculate trackng target gray model. b) Extract the selected target corner pont. c) Regster corner of target of prevous frame to next frame. Usng the affne transformaton gets modfed teraton center. d) Accordng the modfed teraton center to calculate gray model and use the bandwdth of the prevous frame. Then compare Bhattacharyya coeffcent, f t s greater than the gven threshold, go to step g, otherwse go to step e.

4 000 Optoelectroncs Engneerng and Informaton Technologes n Industry e) Calculate Mean-Shft vector. f) Accordng Mean-Shft vector whch s got from e calculate gray model and use the bandwdth of the prevous frame. Compare Bhattacharyya coeffcent of gray models of next frame and prevous frame, f more than a certan threshold value (generally choose 0.9), go to step g, otherwse go to step e. g) Usng the adaptve scale gets the best bandwdth. Test results Ths artcle selects a car whch moves very quckly and non-constant acceleraton. Ths vdeo has 75 frames. Usng Matlab008a fnsh the smulaton. Select the car n the frst frame by usng mouse. RGB color space was quantzed nto 6*6*6 bns. The Bhattacharyya coeffcent s 0.9. The target was more easly to be lost because of ts fast speed. The result of Mean-Shft was shown n Fg.. Because of ts non-constant acceleraton and curve track, the target was also lost. The result of Mean-Shft and Kalman was shown n Fg.. The corner pont extracton k equals to 0.03, threshold s 0.0C max. The sde of square area of coarse matchng s 0. And correlaton coeffcent threshold value s 0.9.The sde of square area of fne matchng s also 0.As was shown n Fg.3. The track algorthm proposed n ths artcle uses the corner features combned wth affne transformaton theory and then usng the Mean-Shft track the target. It has a better effect than Kalman and Mean-Shft. The result was shown n Fg. 4. Fg.. Usng Mean-Shft track the car. The frames,5,45 are shown Fg.. Usng Mean-Shft and Kalman track the car. The frames,5,45 are shown Fg.3. The corner regstraton algorthm whch was used n ths artcle. The frames 4,5 are shown

5 Advanced Materals Research Vols Fg.4. Usng corner regstraton and Mean-Shft track the car whch was proposed n ths artcle. The frames,5,45 are shown Concluson Ths artcle ntroduces Mean-Shft and Kalman algorthm, and ponts out ther shortcomngs. To mprove ther shortcomng, we propose corner regstraton and affne transformaton to mprove teratve center of next frame, then use Mean-Shft to track the target. The expermental results show that ths method s effectve on trackng the fast movng and curve drvng target. References [] YANG Hongxa, HANG Ywen and LIU Xu. Algorthm for Vdeo Target Trackng Based on Mean-Shft and Kalman Flter[J]. Journal of Wuhan Unversty of Technology(Informaton & Management Engneerng). 0, 34()(In Chnese) [] Dorn Comancu and Peter Meer: Mean Shft Analyss and Applcaton Computer Vson[C].The Proceedngs of the Seventh IEEE Internatonal Conference. 999 pp vol. [3] Y. Boers and J.N. Dressen. Multtarget partcle flter track before detect applcaton[j].radar, Sonar and Navgaton. IEEE Proceedngs,0 Dec. 004,Volume: 5, Issue: 6, pp [4] Dorn Comancu,Vsvanathan Ramesh and Peter Meer. Kernel-Based Target Trackng[J]. IEEE Transacton On Pattern Analyss And Machne Intellgence. On May 003,pp VOL.5,NO.5 [5] Khald, M. Sohal:Based nature of Bhattacharyya coeffcent n correlaton of gray-scale targets[c]. Image and Sgnal Processng and Analyss, 005. ISPA 005. Proceedngs of the 4th Internatonal Symposum. 005 pp.09-4 [6] Dng Zhengjan, Ma Ahua: Harrs corner detecton based on the mult-scale topologcal feature[c]. Computer Scence and Network Technology (ICCSNT), 0 Internatonal Conference. 4-6 Dec. 0 vol.3 pp [7] Qan Zhang,Lu Zheng Ka,Pang Yan We and L We. Automatc regstraton aerophotos based on susan operator[j]. Journal of surveyng and mappng. On August 003 pp vol 3, 3 perod.(in Chnese)

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