2014 4th Internatonal Conference on Artfcal Intellgence wth Applcatons n Engneerng and Technology Vehcle Trackng Usng Partcle Flter for Parkng Management System Kenneth Tze Kn Teo, Renee Ka Yn Chn, N.S.V. Kameswara Rao, Farrah Wong, We Leong Khong Modellng, Smulaton & Computng Laboratory, Materal & Mneral Research Unt Faculty of Engneerng, Unverst Malaysa Sabah Kota Knabalu, Malaysa E-mal: msclab@ums.edu.my, ktkteo@eee.org Abstract Increment of on-road vehcles has urged publc venues to provde vstors wth a larger area of parkng space. As the parkng area grew larger for example n a hyper mall, a well-organzed parkng management system s necessary to assst drvers n locatng parkng poston. Besdes, t can also help the management team to montor vehcle flow n the parkng lot. Vehcle trackng plays an mportant role to the parkng management system, as accurate trackng result wll lead to a more effcent management system. Among commercally avalable sensors, vdeo sensor has been commonly deployed n the parkng area due to ts ablty n obtanng a wde range of vehcle nformaton. However, mages captured usng vdeo sensors are lmted under stuatons where vehcles are undergong occluson and maneuverng ncdents. Ths wll cause trackng error therefore affectng the performance of the parkng management system. Partcle flter has been proven as one of the promsng technques to track vehcle under dsturbances. Therefore, partcle flter s proposed to track vehcle under occluson and maneuverng ncdents n ths study. Expermental results show that the partcle flter s able to track a target vehcle under dfferent dsturbances. Keywords partcle flter; vehcle trackng; occluson; parkng lot; vdeo sensor I. INTRODUCTION In ths day and age, cars are the domnant mode of transportaton n most countres. Thus, ensurng enough vacant space n the parkng lot becomes mportant due to the number of vehcles n an urban area has ncreased over the recent years. The newer publc venues wth hgh flow rate of vstors such as shoppng malls, food courts, parks and government admnstraton buldngs are constructed wth more vehcle parkng spaces compared to older venues. Wth such a huge parkng area, a well-equpped and organzed parkng management system s always essental to montor and assst the drvers n localzng ther vehcles. Typcally, an effcent vehcle trackng system conssts of hardware and software. Hardware refers to the devce that s nstalled to obtan the nput nformaton. Meanwhle, software s used to analyze the nput nformaton obtaned from the hardware. The hardware mplemented n the vehcle trackng can be categorzed nto actve sensors and passve sensors [1]. Actve sensors determne the sgnals transmtted by the sensor that was reflected or scattered by the surface of an object. For nstance, some parkng management mplements nfrared or ultrasonc sensors to detect the occupancy of the parkng lot. If the parkng lot s vacant, the lght-emttng dode (LED) wll dsplay green color. Otherwse, t wll show a red color. Based on the color dsplayed by the LED, t wll reduce the drver s searchng tme n approachng the nearest parkng bay. However, the system s cost consumng snce t needs to deploy sensors at each parkng bay. Besdes, the LED emttng the wrong sgnal due to the unknown object blockng the beam from the sensors wll compromse the system accuracy. Thus, t s more effectve to detect the occurrence of the vehcle n a parkng bay, than to track and localze the vehcle. On the other hand, passve sensor such as vdeo cameras provdes frutful nformaton regardng the vehcles, whch s extractable by software. Snce most of the exstng parkng lots have mplemented vdeo sensor for survellance purposes n order to prevent crmnal cases, the vdeo sensors can be used to track the vehcle entry for assstng drvers and montorng vehcle flow n the parkng lot. However, the vdeo nformaton obtaned based on the vehcle s observable outlook wll encounter dffcultes when the target vehcle undergong occluson and maneuverng ncdents [2]. The observable nformaton of the target vehcle wll be lost or changed durng those ncdents, resultng n the trackng dffcultes [3]. Besdes, the dynamc changes of the vehcle flow caused by occluson and maneuverng ncdents wll also create a non-lnear and non-gaussan stuaton. Hence, partcle flter (PF) has been proposed n ths study to overcome the vehcle undergong occluson and maneuverng due to ts ablty n dealng wth the non-lnear and non- Gaussan stuatons. PF s a set of posteror densty estmaton algorthms that estmate the posteror densty of the state-space by drectly mplementng the Bayesan recurson equatons. Although PF s able to overcome dynamc changes caused by the vehcle flow, t has to deal wth partcle degeneracy problem. Partcle degeneracy happens when the varance of the mportance partcles weght contnues to ncrease untl the algorthm s unable to avod the degradaton of the partcles weght. In order to solve the partcle degeneracy problem, partcle resamplng s necessary [4]. Conventonal method of resamplng mght result n partcle mpovershment problem by duplcatng only heavy weght partcles. Thus, a genetc algorthm (GA) resamplng approach wll be mplemented n ths study to track the target vehcle more effcently and effectvely. 978-1-4799-7910-3/14 $31.00 2014 IEEE DOI 10.1109/ICAIET.2014.40 193
II. OVERVIEW OF PARTICLE FILTER The purpose of vehcle trackng s to accomplsh the applcatons goal usng vehcle poston for survellance, localzaton and modellng. The development of vdeo survellance nfrastructure such as the producton of hgh-end computers, the avalablty of hgh qualty vdeo cameras, and the escalatng need for vdeo analyss have ncted the nterest n vsual trackng [5]. Vehcle trackng usng vdeo cameras can estmate the poston of the nterested vehcle correctly n every consecutve frame, but complcatons may arse because of the vehcle moton, occluson and maneuverng ncdents. Throughout the trackng technques usng nformaton extracted from vdeo cameras, PF can track vehcle under occluson and maneuverng ncdents effectvely by estmatng the vehcle locaton through Bayesan probablstc framework [6]. Unlke other trackng algorthms, PF exploted estmaton does not nvolve lnearzaton wth the current estmates. PF approxmates the desre posteror dstrbutons by dscrete random measure, whch s known as the partcles wth assocated weghts, or recognzed as probablty mass [7]. PF s also named as sequental Monte Carlo (SMC) because t mplements a recursve Bayesan flter wth Monte Carlo smulatons. It s an approach that utlzes the sequental estmaton of relevant probablty dstrbutons usng mportant samplng (IS) technques. The estmaton of dstrbuton wll be n dscrete random measure [8]. The key dea s to represent the posteror dstrbuton based on a fnte set of random weghted partcles. PF s used to represent the propagaton condtonal densty dstrbutons (CDD) when the observaton probablty densty dstrbutons (PDD) nvolved n the vehcle trackng process are non-lnear and non-gaussan [9]. In addton, PF technque estmates the current state of the target vehcle based on the prevous observaton states. In vdeo trackng, the observaton state s referred to the features that can be used to represent the target vehcle such as color, shape, edge, texture or any combnatons of features [10]. In ths study, the combnaton of color and shape features has been chosen as the features to represent the target vehcle s model. In estmatng the poston of target vehcle based on Bayesan probablstc framework, the posteror probablty densty functon (PDF) and observaton PDF can be wrtten as p(x t Z t ) and p(z t X t ) respectvely, where X t s the state vector representng the poston of the tracked vehcle and Z t s the state vector representng all the state space that are beng estmated. As mentoned earler, PF s used to approxmate the posteror dstrbuton of the target vehcle based on a fnte set of random weghted partcles, N p. The state estmaton of the target vehcle can be wrtten as shown n (1) where x t represents the locaton of the target vehcle and w t s the weght that s allocated to each partcle (lmted from zero to one). Hence the whole set of partcles can be normalzed to one. S t = {x t, w t } =1,2,3,...,N p (1) The framework of PF can be summarzed n three mportant steps, whch are predcton, measurement and resamplng [11]. Fg. 1 shows a general framework of PF. In the predcton step, a set of random partcles wll be dstrbuted to represent the state transton of the vehcle model based on a Bayesan dstrbuton. In the measurement step, the generated partcles wll be assgned wth weght accordng to the features lkelhood. The resamplng step s used to avod partcle degeneracy by elmnatng and replacng the low weght partcles wth newly regenerated partcles. A. Predcton Step Predcton step s the ntal step that assgns the partcles usng Bayesan dstrbuton. As mentoned earler, the partcles represent the estmated target vehcle s posteror poston. The predctons of the posteror poston begn when the pror poston of the target vehcle s beng dentfed. Based on the pror poston, a set of random partcles wll be dstrbuted to estmate the locaton of the target vehcle. In terms of trackng accuracy, more ntal partcles dstrbuted wll lead to more promsng trackng results or vce versa. However, mplementng a huge amount of partcles wll cause hgher computatonal cost. Snce resamplng step can take place to regenerate and replace the partcles wth low weghtage, the number of ntal partcles can be reduced to an optmal value. The pror poston of the target vehcle can be obtaned by segmentng the target vehcle from the mage sequences. After the pror PDF s obtaned, the PF algorthm can compute the posteror PDF by usng the Bayer s rule as shown n (2). p(x t Z 1:t ) = p(z t X t )p(x t Z 1:t 1 ) p(z t Z 1:t 1 ) Fgure 1. The general framework of partcle flter. (2) 194
B. Measurement Step Measurement step n the PF algorthm computes the weght of the partcles accordng to the lkelhood of shape and color features. The lkelhood can be measured by comparng the model features of the target vehcle wth the feature extracted from the estmated posteror poston. Snce vehcle has a sold structural geometry outlook, the shape and color features are used to represent the vehcle n ths study. The shape feature lkelhood can be measured usng Hausdorff dstance, H dst as shown n (3). ϕ s = 2 H dst 1 2πσ e 2σ 2 (3) The Hausdorff dstance has a lmt from zero to one. Smaller Hausdorff dstance value means the feature lkelhood extracted s more smlar to the model feature of the target vehcle and vce versa [12]. On the other hand, the color feature lkelhood s computed usng Bhattacharyya dstance, B dst as shown n (4) [13]. It s dentcal to the Hausdorff dstance, where smaller dstance value represents smlar features. σ n (3) and (4) s an adjustable standard devaton, whch s chosen accordng to the vehcle trackng cases. ϕ c = 2 B dst 1 2πσ e 2σ 2 (4) In ths study, a fuson of color and shape features has been proposed to compensate the weakness of usng only ether one of them. Although the shape feature has the ablty to track vehcle wth a fxed geometry structure, t s weak n dealng wth the occluson ncdents. The color feature has the ablty to deal wth partal occluson ncdents, but t wll suffer when the color of the target vehcle s smlar to the envronment background or other objects. The weghtage of the fuson of shape and color features can be obtaned through (5), where t has been set to 0.5 n ths study. w t = α(ϕ s )+(1 α)(ϕ c ) (5) After computng the features lkelhood, the weght of the partcles wll be updated accordngly. The set of partcles wll undergo normalzaton through (6). W t = w t N p w =1 t Based on the partcles weghts assgned, the posteror PDF can be obtaned wth (7) to predct the poston of the target vehcle by calculatng the mean value of the predcted state. (6) N p(x t Z 1:t ) p W t δ(x t X t ()) (7) =1 C. Resamplng Step The resamplng step s the most mportant step n the PF algorthm. It s used to avod the partcle degeneracy problem. Partcle degeneracy only becomes apparent after a few teraton of trackng algorthm. Ths happens because partcles experencng low weght wll deal wth contnuously ncreasng partcle weght varance n every consecutve processng frame, unless the correcton step s executed. In order to determne the occurrence of the partcle degeneracy problem, the effectve sample sze for every teraton needs to be calculated. The effectve sample sze can be obtaned through (8). N eff = N p =1 1 (w t ) 2 If the estmaton of effectve sample sze value s less than the threshold value, the partcle degeneracy problem s sad to have occurred. Hence, partcles need to be resampled n order to mprove the predcton of the target vehcle s posteror poston. III. GENETIC ALGORITHM BASED RESAMPLING APPROACH The resamplng step can assst n producng better posteror PDF. However, wthout an approprate resamplng method, t wll cause ssues to the trackng algorthm. For nstance, the famous resamplng technque, samplng mportance resamplng (SIR) causes the partcle mpovershment problem. SIR resamplng takes place by duplcatng the heavy weght partcles to replace the elmnated low weghtage partcles. In ths case, the partcles generated wll contan many-repeated posteror poston. After a few teratons, all partcles mght collapse nto to a sngle poston wth the heavest weghtage. Thus, the trackng algorthm enables to track the target vehcle contnuously especally when there are dsturbances. In order to control the dversty of the partcles, GA s mplemented n ths study to ncrease the convergence rate of estmatng the posteror dstrbuton. GA s known to be a powerful algorthm for searchng through large and complex soluton space consstng of multple local mnma. Unlke the determnstc methods, GA works based on randomzaton [14]. The operaton of GA s based on the entre populaton of postons, nstead of searchng from a sngle poston. Ths contrbutes to the robustness of GA and mproves the probablty to reach the global optmum, thus reducng the chance of beng trapped n a local statonary poston. Hence, the characterstcs of GA have proven that t has the advantage over the convergence of the posteror PDF estmaton. In ths study, the ftness functon of GA s based on the weght of the partcles, (8) 195
whereas the stoppng crteron for GA s based on the effectve sample sze as shown n (8). GA can be categorzed nto three stages, whch are selecton stage, crossover stage and mutaton stage. Selecton stage s mportant n choosng the qualty partcles to regenerate the chldren solutons. There are many ways to select the parent partcles for crossover. In ths study, rank selecton s beng selected, so that all the partcles wll be ranked n descendng order accordng to the weghtage. After all partcles are ranked, the algorthm wll randomly select two partcles as the parent to generate two new posteror postons. Snce partcles wth heaver weghts wll be assgned the hgher rank, the probabltes for the heavy weght partcles to be selected s hgher as compared to those partcles wth lower weght. After the parents selecton, the crossover takes place by combnng the propertes of the parents [15]. In ths study, arthmetc crossover wll be mplemented snce t can produce a new soluton that contans the characterstcs of both parents. The arthmetc crossover s formulated by usng (9) and (10). C1 = P1 α + P2 (1 α) (9) C2 = P2 α + P1 (1 α) (10) where P1 and P2 are the parents selected to perform crossover, α s the weght factor wth the range from 0 to 1, and C1 and C2 are the chldren solutons generated from the crossover process. Weght factor of 0.7 s chosen n ths study. It represents that 70% of the frst parent partcle characterstcs wll combne wth 30% of the second parent partcle characterstcs to produce a new partcle, nversely for the second new partcle beng generated. Implementng arthmetc crossover n the algorthm can produce chldren partcles that preserve most of the parent characterstcs wthout affectng the speed of convergence. Hence, a more accurate trackng result can be computed. After the crossover stage, the chldren partcles wll undergo mutaton stage before beng accepted by the trackng algorthm. Actng as a fnal checkpont to recover the usable nformaton that mght be lost durng the selecton and crossover stages, the mutaton stage prevents the populaton generated from beng stagnated at the local optmal poston. Moreover, the mutaton stage wll only be actvated when the chldren partcles ht the defned mutaton rate. Although mutaton stage can prevent the partcles from trappng at local maxma, the rate of mutaton needs to be farly low to avod the loss of healthy partcles, whch eventually affects the convergence rate. In ths study, the mutaton rate s set to be 1%. IV. RESULT AND DISCUSSION In ths study, the vehcle trackng result usng GA resamplng approach s shown n Fg. 2 and Fg. 3. Fg. 2 shows the result of the target vehcle undergong occluson ncdents. Meanwhle, Fg. 3 shows the trackng result whereby the target vehcle s undergong maneuverng n the parkng lot. The vdeo was captured n a parkng lot at a shoppng mall and the frame rate has been set to 30 frames per second. The ntal amount of partcles for vehcle trackng was set to be 200 partcles. As shown n Fg. 2 and Fg. 3, the sold boundary box denotes the border of the vehcle beng tracked. Meanwhle the cross con ndcates the predcted posteror poston of the target vehcle. The predcted poston of the target vehcle s estmated based on the mean value calculated from the posteror PDF. From Fg. 2, the vehcle trackng process conssts of three stages, whch are the target vehcle wthout beng occluded, partally occluded and fully occluded. At Frame 48, the target vehcle s movng wthout any occluson. From the result, the algorthm can locate and predct the locaton of the target vehcle. When t comes to Frame 72 and Frame 88, the target vehcle s partally occluded by another movng vehcle from the opposte drecton. Normally, the conventonal trackng algorthm wll track the wrong target at ths moment because the features of the target vehcle wll be nfluenced by the other vehcle, whch s nearer to the vdeo camera. However, the proposed GA based PF algorthm s able to track the target vehcle accurately. Frame 48 Frame 72 Frame 88 Frame 104 Frame 120 Fgure 2. Result of vehcle trackng under occluson ncdents. 196
target vehcle n unt of pxel. Meanwhle, x o and y o are the orgnal coordnate of the vehcle n unt of pxel. Frame 30 Frame 86 Frame 142 Frame 294 RMSE = (x e x o ) 2 + (y e y o ) 2 (11) Fg. 4 shows the RMSE versus frame ndex for vehcle trackng under occluson ncdent. RMSE calculates the dfference between the estmated posteror postons wth the orgnal poston of the target vehcle. From the result, the RMSE from Frame 72 to Frame 112 s hgher because the obstacle vehcle nfluences the target vehcle s features when undergong occluson. However, the RMSE s consdered acceptable because the hghest dfference s only 45 pxels away from the orgnal poston. When the target vehcle reappears from the occluson, the algorthm s able to track the target vehcle wth just 6 pxels away from the orgnal poston. It can be concluded that the proposed GA based PF algorthm s able to track the target vehcle under occluson ncdents. Fg. 5 shows the trackng performance for target vehcle undergong maneuverng and partal occluson by the column structures of the buldng. By comparng Fg. 4 and Fg. 5, the average RMSE from Fg. 5 s hgher than Fg. 4 because the target vehcle undergoes scale nvarant and shape changng when t s maneuverng. Ths ncreases the dffcultes n trackng the target vehcle. However, PF manages to track the target vehcle wth the maxmum of 60 pxels away from the orgnal poston. Frame 382 Fgure 3. Result of vehcle trackng under maneuverng ncdents. Another crucal moment takes place when the target vehcle reappeared after beng occluded by the movng vehcle as shown n Frame 104 and Frame 120. From the results, the trackng algorthm s able to robustly track the target vehcle mmedately after the occluson s completed. Besdes, the algorthm can also recover the nformaton of the target vehcle and resume the trackng process wthout any delay. Hence, t shows that GA based PF algorthm s sutable to be mplemented n the parkng lot that conssts of two-way drectons. As shown n Fg. 3, the target vehcle undergoes maneuverng and partally occluded by the column structures of the buldng. Generally, trackng a target vehcle wth maneuverng s a challengng task due to the outlook features of the target vehcle that s always changng. In order to robustly track the target vehcle, the features must be updated to the developed algorthm before estmaton of posteror poston s performed. Throughout the vehcle trackng results shown n Fg. 3, the PF technque has the ablty to deal wth the maneuverng ncdents. The performance of the vehcle trackng result can be calculated usng root mean square error (RMSE) as shown n (11), where x e and y e are the estmated coordnate for the Fgure 4. RMSE VS FRAME INDEX for target vehcle undergong occluson ncdent. Fgure 5. RMSE VS FRAME INDEX for target vehcle undergong maneuverng ncdent. 197
V. CONCLUSION A well-organzed and effcent vehcle parkng management s needed to manage the vehcles survellance and localzaton n huge parkng area. Vdeo cameras that are commonly nstalled n the parkng area can be useful resources to perform vehcle trackng, but they are normally fxed at a statc poston, resultng n occluson and maneuverng ncdents that affects the trackng accuracy. The proposed GA resamplng based PF algorthm has successfully tracked the target vehcle undergong occluson and maneuverng ncdents n a parkng lot because GA has the ablty to converge the estmated posteror poston nstead of dverge to the wrong poston. Thus, t can be concluded that the proposed vehcle-trackng algorthm s capable of robustly trackng the target vehcle under dfferent types of dsturbances. ACKNOWLEDGMENT The authors would to acknowledge the Mnstry of Educaton Malaysa (KPM) for supportng ths research under Exploratory Research Grant Scheme (ERGS), grant no. ERG0042-ICT-1/2013. REFERENCES [1] T. Rabe, A. Shalaby, B. Abdulha, and A. El-Rabbany, Moble Vson-based Vehcle Trackng and Traffc Control, 5 th Internatonal Conference on Intellgent Transportaton Systems, 2002, pp. 13-18, do: 10.1109/ITSC.2002.1041181. [2] W.Y. Kow, W.L. Khong, F. Wong, I. Saad, and K.T.K. Teo, Adaptve Trackng of Overlappng Vehcles va Markov Chan Monte Carlo wth CUSUM Path Plot Algorthm, 3 rd Internatonal Conference on Computatonal Intellgence, Communcaton Systems and Networks, 2011, pp. 253-258, do: 10.1109/CICSyN.2011.61. [3] A. Dore, A. Beoldo, and C.S. Regazzon, Multple Cue Adaptve Trackng of Deformable Objects wth Partcle Flter, 15 th IEEE Internatonal Conference on Image Processng, 2008, pp. 237-240, do: 10.1109/ICIP.2008.4711735. [4] J. Yu, W. Lu, and Y. Tang, Adaptve Resamplng n Partcle Flter Based on Dversty Measures, 5 th Internatonal Conference on Computer Scence & Educaton, 2010, pp. 1474-1478, do: 10.1109/ICCSE.2010.5593746. [5] H. Yang, S. Lng, Z. Feng, W. Lang, and S. Zhan, Recent Advances and Trends n Vsual Trackng: A Revew, Neurocomputng, vol. 74, no.18, 2011, pp. 3823-3831, do: 10.1016/j.neucom.2011.07.024. [6] M. Ln, F. Pan, J. Wang, and S. Chen, Vehcle Trackng Based on Partcle Flter Usng Double Features, Internatonal Conference on Informaton Engneerng and Computer Scence, 2009, pp. 1-4, do: 10.1109/ICIECS.2009.5362802. [7] P.M. Djurc, J.H. Kotecha, J. Zhang, Y. Huang, T. Ghrma, M.F. Bugallo, and J. Mguez, Partcle Flterng, IEEE Sgnal Processng Magazne, vol. 20, no. 5, 2003, pp. 19-38, do: 10.1109/MSP.2003.1236770. [8] J.V. Candy, Bayesan Sgnal Processng Classcal, Modern, and Partcle Flterng Methods, US: A John Wley & Sons, Inc. 2009. [9] H. L, Y. Wu, and H. Lu, Vsual Trackng usng Partcle Flters wth Gaussan Process Regresson, Advances n Image and Vdeo Technology and Lecture Note n Computer Scence, vol. 5415, 2009, pp. 261-270, do: 10.1007/978-3-540-92957-4_23. [10] M.Y. Choong, W.L. Khong, W.Y. Kow, L. Angelne, and K.T.K. Teo, Graph-based Image Segmentaton usng K-Means Clusterng and Normalsed Cut, 4 th Internatonal Conference on Computatonal Intellgence, Communcaton Systems and Networks, 2012, pp. 307-312, do: 10.1109/CICSyN.2012.64. [11] W.L. Khong, W.Y. Kow, F. Wong, I. Saad, and K.T.K. Teo, Enhancement of Partcle Flter Approach for Vehcle Trackng va Adaptve Resamplng Algorthm, 3 rd Internatonal Conference on Computatonal Intellgence, Communcaton Systems and Networks, 2011, pp. 259-263, do: 10.1109/CICSyN.2011.62. [12] S.C. Park, S.H. Lm, B.K. Sn, and S.W. Lee. Trackng non-rgd Objects usng Probablstc Hausdorff Dstance Matchng, Journal of Pattern Recognton, vol. 38, no. 12, 2005, pp. 2373-2384, do: 10.1016/j.patcog.2005.01.015. [13] M.S. Khald, M.U. Ilyas, M.S. Sarfaraz, and M.A. Ajaz. Bhattacharyya Coeffcent n Correlaton of Gray-Scale Objects, Journal of Multmeda, vol. 1, no. 1, 2006, pp. 56-61, do:10.4304/jmm.1.1.56-61. [14] Y.S. Cha, Z.W. Sew, A. Krng, S.S. Yang, and K.T.K. Teo, Adaptve Hybrd Channel Assgnment n Wreless Moble Network va Genetc Algorthm, 11 th Internatonal Conference on Hybrd Intellgent Systems, 2011, pp. 511-516, do: 10.1109/HIS.2011.6122157. [15] M.K. Tan, Y.K. Chn, H.J. Tham, and K.T.K. Teo, Genetc Algorthm Based PID Optmzaton n Batch Process Control, IEEE Internatonal Conference on Computer Applcatons and Industral Electroncs, 2011, pp. 162-167, do: 10.1109/ICCAIE.2011.6162124. 198