A Suspect Vehicle Tracking System Based on Video

Transcription

1 3rd Internatonal Conference on Multmeda Technology ICMT 2013) A Suspect Vehcle Trackng System Based on Vdeo Yad Chen 1, Tuo Wang Abstract. Vdeo survellance systems are wdely used n securty feld. The large amounts of vdeo data make t tme-consumng for crmnal nvestgaton. Tradtonal researches conducted on vsual analyss are extremely challengng, due to the complexty of scenes, such as large varaton n vewng angle and multple movng objects. Ths paper approaches these problems by proposng a vdeo based suspect vehcle trackng system. We used a road modellng based on spatal clusterng to obtan colour road nformaton. In order to mprove the accuracy of target matchng, we ft the 3-D model wth regon of nterest (ROI) to the 2-D target mages, and compute the hstogram n ROI as the feature for target matchng. Experment shows that system can provde accurate result for suspect vehcle trackng. Keywords: Survellance vdeo; Vdeo analyss; 3-D model; ROI 1 Introducton Wth the fallng cost of mountng cameras to capture vdeo magery, a wde area of urban road ntersectons has been covered wth vdeo survellance for statstcal and securty purpose [1 2]. Although vdeo survellance s a great help for polce to locate the suspect on the run and fnd evdences, t also causes another problem for manual search. After a crme occurs, nvestgator has to go through the contnuous 24-hour montorng vdeo captured by multple cameras to search for a partcular target, whch can be very tme-consumng and labor-ntensve. What s needed s a vdeo-based system to search for the target automated and provde the nvestgator wth the precse result. Although over the last decades, numerous researches conducted on vsual analyss Y. Chen ( ) The School of Electronc and Informaton Engneerng, X an Jaotong Unversty,710049,X an, Shaanx, P.R.Chna e-mal: chenmodd@gmal.com T. Wang The School of Electronc and Informaton Engneerng, X an Jaotong Unversty,710049,X an, Shaanx, P.R.Chna e-mal: twang@se.xjtu.edu.cn The authors - Publshed by Atlants Press 200

2 of traffc scenes [3,4] concerns about advanced algorthms related to background modelng, target trackng and classfcaton, few of them pay much attenton to the target matchng wth dfferent posture n multple cameras. On the bass of the above consderaton, we propose a vdeo-based computer vson system that enables a sngle operator to search for a specfc target through a dstrbuted network of actve vdeo sensors. It s flexble wth complex area such as dfferent camera postons and multple movng objects. The man contrbuton of ths paper s lsted as follows: In order to obtan colored road nformaton, a road modellng method based on spatal cluster s used for background modelng. A 3D model based strategy s employed to locate our defned regon of nterest (ROI), such as the front and the back of the vehcle. The paper s organzed as follows: In Sec 2, we present the bref ntroducton about the three-ter herarchy archtecture desgned for parallel processng on multple servers, wth the descrpton of key technologes n Sec 3. The expermental results are lsted n Sec 4 and conclusons wth plan for future research are drawn n Sec 5. 2 System Archtecture As a three-ter herarchy archtecture, our system from bottom to top can be dvded nto three level: the underlyng, the mddle and the top, as shown n 0. Fg.2.1.System Dagram. The top of the system s called user nteracton, whch conssts of system ntalzaton, user nput and result dsplay. System ntalzaton module s used to provde confguraton nformaton for computng the sze and speed of object. For each camera, parameters ncludng optcal axs deflecton angle, focal length, and CCD area, the heght and locaton of the camera are usually avalable, or f unknown, they can be calbrated by the nteractve tools [5,6]. For user nput module, the sample mage of the target to search can be submtted from a specal player wth the screenshot functon for user to 201

3 select the target from a pece of vdeo. Then a method called grab cut wll be employed to the sample mage, to segment the target from the background for further processng. As for result dsplay module, a report contans every detal of the matched target wll be dsplayed to the user on the dgtal map based on camera locaton, along wth the sze, speed and evdence vdeo peces of the target. The mddle level s called the task schedules, whch s able to handle multple parallel operatons runnng smultaneously. For each user nput from the top, new task s created and scheduled for further processng by servers n the underlyng. After all the results are collected from the underlyng, the mddle level wll generate a report whch wll be submtted to the top for result dsplay. The underlyng level represents the parallel applcatons for suspect vehcle trackng on clusters of workstatons, ncludng background modelng, target trackng, target classfcaton and target matchng. The background modellng gets the background nformaton by obtanng the colour road nformaton from the vdeo sequences. In the target trackng module, after the nose removal, movng subjects are marked usng the connected doman algorthm and tracked usng a global nearest neghbor optmzaton that conssts of both appearance and dynamc nformaton. As a result, each movng target n the background-subtracted mage wll be detected and traced untl leavng the scene. Meanwhle, the target classfcaton module wll classfy each vehcle detected nto sx categores: hatchback, car, SUV, van, bus and truck. Only the vehcles that match the category of the user nput wll be delvered to the target matchng module. In ths module, the feature of the ROI s calculated usng a 3D model based strategy to measure the dstance between the nput sample and the detected vehcle. The result wll be reported to the mddle layer when the task s fnshed. 3 Key Technologes 3.1 Roads Background Modellng The most commonly used methods for background modelng are frame dfference, the mean background modelng, optcal flow method, Gaussan background modelng and Bayesan background modelng. Because of the complexty of traffc scene, the methods above all show a great defcency for target matchng. Frame dfference, the mean background modelng and optcal flow method have poor adaptablty to envronmental change. Optcal flow modelng s more accurate, but the computatonal complexty can hardly meet the requrement of the system. And Gaussan mxture modelng requres the assumpton that the background pxels follow a known dstrbuton, whch does not meet the fact that the background of the transport network follows non-parametrc dstrbuton. Although Bayesan modelng can be performed n color space, the accuracy s not acceptable when the background color s smlar to the foreground, whch often occurs n traffc scene. What s needed s methods that can accurately obtan the colored road nformaton. 202

4 Fg.3.1.The schematc dagram of color dstorton. Statstcal analyss has shown that the pxels n a specfc poston of the road are always wthn a range of fluctuaton, whch follows the rules of color dstorton, as s shown n Fg.3.1. Kyungnam Km confrmed by experment that, n the RGB color space, wth the llumnaton change, most of the pxels values on the color template are changng along wth the axal drecton of the orgn pont [7]. Based on the consderaton above, we employed the rules of color dstorton and brghtness dstorton as the crtera for cluster analyss, and each cluster represents a path model possblty. Thus, we can estmate whether the pxels are background n color space and avodng the splttng case that occurs when the color of the subject s smlar to the background. 3.2 ROI Strategy Based on 3D Model To search for the suspect vehcle s essentally a matchng problem. The tradtonal feature-based vehcle matchng method, such as the headlghts matchng whch requres the frontal vew of the vehcle, s found to be low and flexble to camera vewponts. Other vehcle matchng method usng shape and appearance technques can only make dstncton between dfferent categores other than the subjects of the same category. In order to provde an accurate result for suspect vehcle trackng, we proposed the ROI strategy based on 3D models for feature extracton. The experment result shows that matchng accuracy can be ncreased by usng 3D vehcle model fttng method [8], whch ams at classfyng target nto dfferent categores of vehcle. Based on the related works, we buld sx models represent for hatchback, sedan, SUV, van, bus and truck. After the system s ntalzed, a lst of the possble 3D models s complete wth the locaton and orentaton on the road plane. In target classfcaton module, the detected subject wll be classfed nto the sx categores, usng the appearance vector to measure the smlarty between the subject and each model. As for target matchng, the expermental results show that the features extracted from the nformatve regon such as the front are more representatve than a whole vehcle. The ROI was the feature-rch regons defned on the 3D vehcle models, see Fg 4. For each vehcle detected n the vdeo, gven ntal pose and poston parameters, 203

5 the ROI on the 3D model representng the category of the target s projected nto 2D mage, to generate a hypothetc locaton of the nformatve regon. We mark each regon wth a certan color for further processng. Features of the same ROI wll be measured n target matchng module. 3.3 Target Matchng In target matchng module, the smlarty between the detected vehcle and the sample mage of the suspect vehcle s calculated. Although the camera angles vary, the same regon of the vehcles s detected usng the ROI strategy based on 3D models. Ths allows us to reduce the false match caused by dversty of vewponts. The sample mage nput by user has been processed n the top layer, whch nclude the segment of the target from the background, the ROI determned by user, the category that the vehcle belongs to and the workstatons that need to track the suspect vehcle from survellance vdeos. Accordng to the user operaton, the task schedules are generated to the specfc workstatons to search for the vehcle of specfc category. Thus, the result of classfcaton wll determne whether the detected vehcle wll be processed n target matchng module, and dscard the unqualfed ones. Due to the varety of the camera angles and vehcle poses, the ROI wll be dfferent n the sample mage and detected vdeo. It s mportant to fnd correspondng ROI, whch can be based on the color of the regon to dstngush. We defne the smlarty between the detected vehcle and the sample mage by the summaton of the dstance of the color hstogram d of each correspondng ROI, multplyng wth weght coeffcent w. w w j S (1.1) S d ( H, H ) ' ' 1 2 j H ( j) H ( j) ' 2 ' 1 2 ( H ( j) ) ( H ( j) ) ' 2 ' (1.2) d d w (1.3) S represents the area and 1 H s the hstogram of the ROI. Fg.3.2.The3D models wth regon of nterest (ROI) used for matchng 204

6 4 Result and Analyss of Experment In ths secton, we demonstrate the proposed system. The analyss of the rest modules are shown as follows. We compared the effectveness and effcency of the methods of road modelng and target matchng wth the tradtonal methods. (a) (b) (c) (d) Fg.4.1.(a) The orgnal mage, (b) A result of background modelng usng Bayes, (c) A result of background modelng usng Gaussan, (d) A result of background modelng usng the method of our system. (a) (b) (c) (d) (e) Fg.4.2.The fve orgnal mages captured n 3 dfferent ntersectons. Fg (a), Fg (b) and Fg(c) belongs to the same type of tax, whle Fg(d) and Fg(e) blong to the other type of tax. (a) (b) (c) (d) (e) Fg.4.3.The segement results of the fve pctures. (a) (b) (c) (d) (e) Fg.4.4.Projectons of the 3D model on a vrtul road plane that the target belongs to. (a) (b) (c) (d) (e) Fg.4.5.Front face segment results, each corresponds to the red regon of nterest on the 3D model. 205

7 (a) (b) (c) (d) (e) Fg.4.6.Car roof segment results, each corresponds to the yellow regon of nterest on the 3D model. Fg.4.7.The target matchng results of the fve mages n Fg 7. The frst four cases represents the matchng results of the same vehcle, whle the last sx cases represents the dfferent vehcles. The proposed matchng strategy ncreases the matchng rate of the same objects under dfferent perspectves, and reduces the rate of the dfferent. By usng the ROI strategy based on 3-D models, a threshold of 0.85 can be selected to dstngush the same objects from the dfferent ones, whle the classcal hstogram matchng method makes t dffcult to select a threshold. We compared the mages processed by Gaussan background modelng, Bayesan background modelng and the method based on spatal cluster, shown n Fg.4.3. The result shows that a foreground object s lost when employng the Bayes, due to the background color s smlar to the foreground object. The result of usng Gaussan s also not acceptable for the same reason. 0shows the result of comparng the tradtonal matchng method wth the ROI strategy based on 3-D models. The hstogram ndcates that the color dstrbuton of the ROI can descrbe the feature of the target better than the whole regon. 5 Concluson In ths paper, wth respect to the problem of trackng suspect vehcle n vdeos captured by multple cameras, we proposed a suspect vehcle trackng system. Accordng to the experments, the results for road modelng are much better than that of Bayes and Gaussan back ground modelng. And the 3D model-based method s 206

8 adaptve to dfferent cameral poston. The system can provde accurate result for suspect vehcle trackng. 6 Acknowledgement Ths research was supported by the Natonal Scence Foundaton of Chna (NSFC n short) under the grant No References 1. Wemng Hu ; Xe, D. ; Zhouyu Fu ; Wenrong Zeng ; Maybank, S. " Semantc-Based Survellance Vdeo Retreval [J]". Image Processng, IEEE Transactons on, Ra ty, T.D. "Survey on Contemporary Remote Survellance Systems for Publc Safety". Systems, Man, and Cybernetcs[J]. n IEEE Transactons on, A. Elgammal, R. Duraswam, D. Harwood, and L.S. Davs. Background and foreground modelng usng nonparametrc kernel densty estmaton for vsual survellance, Proc IEEE 90(7), 2012,pp Je Shao, Zhen Ja, Zhpeng L, Janwe Zhao, Pe-Yuan Peng. Feedback strategy on real-tme multple target trackng n cogntve vson system[j]. n Optcal Engneerg,2011, vol Messelod, Stefano, Carla Mara Modena, and Mchele Zann. "A computer vson system for the detecton and classfcaton of vehcles at urban road ntersectons." Pattern analyss and applcatons 8.1-2,2005, pp Worrall, A. D., G. D. Sullvan, and K. D. Baker. "A smple, ntutve camera calbraton tool for natural mages." Proc. 5th Brtsh Machne Vson Conference. Vol. 13. No Km, Kyungnam, et al. "Background modelng and subtracton by codebook constructon." Image Processng, ICIP' Internatonal Conference on. Vol. 5. IEEE, Leotta, Matthew J., and Joseph L. Mundy. "Vehcle survellance wth a generc, adaptve, 3d vehcle model." Pattern Analyss and Machne Intellgence, IEEE Transactons on 33.7 (2011):