timestamp, if silhouette(x, y) 0 0 if silhouette(x, y) = 0, mhi(x, y) = and mhi(x, y) < timestamp - duration mhi(x, y), else

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1 3rd International Conferene on Multimedia Tehnolog(ICMT 013) An Effiient Moving Target Traking Strateg Based on OpenCV and CAMShift Theor Dongu Li 1 Abstrat Image movement involved bakground movement and target movement. The omplexities of movement inreased the diffiult of target traking. This stud was implemented in the Window platform and the Mirosoft visual C ompiler environment, and used amera as a video apture devie. The sstem of moving target detetion and traking was realized based on ontinuousl adaptive mean shift and open omputer vision theor, ahieved moving target traking b improving the algorithm. The experimental results showed that the proposed moving target detetion algorithm was superior to the traditional mean shift traking. It ould trak target effiientl, and further real-time drew motion trajetor. Kewords: Objet traking, Moving detetion, Color histogram 1 Introdution Target traking is an important researh topi and ke tehnolog in visual surveillane, the theor is determined eah frame in the motion state of researh target in the sequene images, and realize the real-time traking of the targets. At present, one of the ke tehnologies in intelligent video appliations were separating moving objet from the video, deteting and extrating the researh objets[1]. Moving objet detetion tehnolog ould be used to detet, lassif and trak objets in the sope of the amera monitoring the sene, and ould be applied to all kinds of monitoring purposes. Suh as illegall parked vehiles detetion, perimeter seurit and intrusion detetion[]. On aount of its broad appliation prospets and signifiant advantages, the researh on moving objet detetion and traking sstem has important signifiane for intelligent video monitoring sstem development[3]. In this paper, moving objet traking sstem was designed using OpenVC whih is Intel open-soure funtion database, it ould invoke the video streaming of amera diretl, and trak moving objet using the ommon Visual 1 D. Y. Li ( ) Department of phsis and Eletroni Engineering, Qujing Normal College, Qujing, China sm895@163.om 013. The authors - Published b Atlantis Press 40

2 ++ development platform. The advantages of this tehnolog were short sstem development le, simple sstem framework and so on. At present,the arithmeti of bakground subtration, optial flow estimation and digital image differene were used to stud moving target detetion ommonl[4]. Due to the error at the proess of image aquisition, the hange of beam on the bakground and other fators, the effet of the arithmeti of bakground subtration had been affeted. The advantages of different method were simple algorithm framework and good real time, but the shortomings were existed ghost image and disontinuous ontour of traking image. Analzed and ompared the arithmeti of bakground subtration, optial flow estimation and digital image differene,the improved algorithm of three frame differene was used to stud the objet traking. The improved algorithm update the motion histor image to eliminate ghost image,and expansion operation to dispel the disontinuous ontour of traking image. This method performs the real time traking target under omplex bakground[5]. The improved algorithm proedures are as follow: Deteted moving objet b three frame differene algorithm. At first, the algorithm alulates the absolute differene d( k 1, k) and d ( kk, + 1) of adjaent two frames image. Seondl, binar proess was used to threshold values, and obtained the binar image b( k 1, k) and b ( kk, + 1). At last, the position of ever pixel in the image b( k 1, k) and b ( kk, + 1) b using the Boolean and operator, and got the binar image D k of three differene algorithm,that is moving objet. Updated motion histor image to eliminate ghost image. In the MHI(Motion Histor Image),the move our pixel was set to urrent time, the moving last long time pixel was eliminated. The funtion of update motion histor image was showed below. timestamp, if silhouette( ) 0 0 if silhouette( ) = 0, mhi( ) = and mhi( ) < timestamp - duration mhi( ), else In the funtion, timestamp is urrent time, duration is maximum duration time of moving traking, silhouette is image mask ode, there are nonzero pixel onsist in moving frame. 41

3 Expansion operation b 3 3 square-shaped struture elements to dispel the disontinuous ontour of traking image. Based on the harateristi of image, irular-shaped or square-shaped struture elements ould be used to dispel the disontinuous ontour of traking image. Beause the results of differene algorithm inlude the square-shape inanition, so square-shape struture elements method was used to dispose the image. The 3 3 square-shaped struture elements method is optimal seletion. Moving Objet Traking Algorithm OpenCV was provided b Intel Compan, whih is open omputer Vision. It is onsist of a serial of C funtions and C++ funtions, and inlude three hundreds of API. The appliation developers ould familiarize the design possess of image, video disposal and omputer vision rapidl. The well transplantable harater and unified the framework of OpenCV ould shorten the development le of sstem design, and the sstem is more stable. Base on the result of this algorithm, if the deteting the moving objet whih tall with requirement, the sstem will trak the moving objet automatiall. The works that follow will ameliorate the algorithm using CAMshift program, and ahieve traking the moving objet. Choosed the position and size of initail searh window RGB image HSV image Set alulation area in searhing window olor histogram olor probabilit distributions Set the enter of window using onvergent entroid, and set the size of window using area Get onvergent entroid ( x, ) Seek area entroid in searh window Set the enter of window at the entroid Convergene? Y N Fig. 1 The flow hart of CAMShift traking algorithm 4

4 CAMShift (Continuousl Adaptive MeanShift) algorithm trak the objet b deteted the olor information of moving objet in the video. Figure 1 showed the flowhart of this algorithm. At first, ever frame piture was translated into the form of HSV olor spae. The entroid and size was obtained b CAMShift algorithm to alulate the olor probabilit distributions[6]. The dashed retangle is hardore of MeanShift program. The main algorithm was omposed of three parts: Obtain the moving objet area b the moving target detetion part, and made the area as hoose ROI, that is hoose the initial searh window. Bak Projet is alulate the olor histogram of traked target. In the ever olor spae, onl the HSV spae had the H weight to denote olor information. So in the alulation proess, other olor spae must be transform to HSV spae, and get H weight to ompute 1D histogram of an image[7]. Aording to olor histogram, the original image was transformed to olor probabilit distributions image, this proess was defined as Bak Projetion. Seek the entroid at urrentl frame b MeanShift algorithm. Choose the ROI area, and get the information of position and size. At first, alulated the entroid of window, and set the enter of window at the entroid. Repeat the steps until the enter of window is onvergene. When seek the entroid at urrentl frame, the zero order moment must be obtained, it indiate the size of the area. the zero order moment was defined as follow: M 0,0 = [ Ix (, )] (1) x, Calulated the first order moment of X and Y: M = 1,0 [ Ixx (, ) ] () M = [ I( ) ] 0,1 In this funtion, I( ) is the pixel of oordinate( ),the range of variants x and was the searh sope. Calulate the entroid of window was ( ) : x = M / M = M / M (3) 1,0 0,0 0,1 0,0 Get the diretion angel, minor axis and major axis of traking objet b alulate seond order moment. The seond order moment expressions as follow: M = [ Ixx (, ) ],0 M = [ (, ) ] 0, I x (4) M = [ I ( ) x] 1,1 The diretion angel of objet major axis expressions as follow: 43

5 1 θ = artan ( M / M x) 1,1 0,0 ( M / M x ) ( M / M ) 0, 0,0,0 0,0 (5) In the expressions (6), a, b and was defined: a = M / M x,0 0,0 b= M / M x 1,1 0,0 = M / M 0, 0,0 In the image, the length of objet major axis and minor axis was alulated aording to the follow funtion: I = W = ( a + ) + b + ( a ) ( a + ) b + ( a ) Using the CAMShift algorithm to ahieve the target traking. MeanShift algorithm was implemented to all frames of order images, and the alulation result of last frame was set as initial value of CAMShift algorithm searh window of next frame. This iterative proess ma be ontinued until some riterion for onvergene is met, and final realized the traking to the targets with high preision. Programming:Aording to the algorithm,vc++ based on Windows was hosen as ompiled platform. The funtion of OpenCV database was used to realize moving target deteting and traking algorithm. As following, entroid projetion algorithm was used to loated enter of alulation target. At last, improved CAMShift algorithm alulate the entroid information whih obtained from entroid projetion algorithm, and realize the real-time traking of the targets. Figure showed the neat flow hart of improved algorithm program. (6) (7) 44

6 Read dates from amera Alloating memor and establish window Finish? N Obtain video Y Target traking b CamShift algorithm Release memor Bakground extration algorithm Close windows Get target b three frame differene algorithm End Centroid projetion algorithm Centroid information Fig. The neat flow hart of improved algorithm program. 3 Results and Disussion The target traking sstem platform was omposed of omputer, displa and amera. The realization algorithm is aomplished b VC++ platform. The video was obtained b a USB amera at ampus, image size is pixel, the frame rate varies is 10 frame per seond. Compared the performane of the improved algorithm with the mean-shift algorithm, it was observed that both the improved and mean-shift algorithm were able to trak the objets at mostl frames. Figure 3 showed the effiient traking result of proposed sstem with improved CAMShift algorithm. Analzed the experiment video, Figure 3(a) showed that two objets our at 14th frame, the one is objet 1 and the another is objet. The objet was not be deteted due to the olor was ver similarit in the bakground. (a) 14 th frame (b) 69 th frame 45

7 () 86 th frame (d) 443 th frame (e) 555 th frame (f) 58 th frame (g) 691 th frame (h) 78 th frame (i) 744 th frame (j) 817 th frame (k) 86 th frame (l) 975 th frame Fig. 3 Seleted frames of the traking results from the video 46

8 4 Conlusion In this stud, an effiient deteting and traking moving target sstem was proposed based on OpenCV funtion database platform. The known disadvantage of this sstem is that it had simple implementation platform and shorten periods. The improved algorithm showed its adaptation when the bakground was omplex or there are objet/bakground appearane variations. It had nie adaptations of different oasion b ordinar mend. In addition, we also showed that an optimisti traking design still provides aeptable traking aura and time onsuming. The results showed that the improved algorithm was superior to the traditional MeanShift traking algorithm. The objets ould be deteted effiientl, and further drew motion trajetor, its alulation omplexit is muh less than traditional MeanShift traking. Aknowledgment This stud is funded b the siene foundation of Qujing Normal College (Grant No. 010QN06) Referenes 1. Babu R. V., Sures S. and Makur A.(010) Online adaptive radial basis funtion network for robust objet traking. Computer Vision and Image Understanding, 114(3): Liu Z., Shen H., Feng G. Y. and Hu D.W. (01) Traking objets using shape onext mathing. Neuroomputing, 83(4): Belaroussi R. and Milgram M.(01) A omparative stud on fae deteting and traking algorithm. Expert sstems with Appliations, 39(8): Mazinan A. H. and Latifi A.(01) Appling mean shift information and Kalmana filtering approahes to objet traking. ISA Transations, 51(3): Yao A. B., Lin X. G., Wang G. J. and Yu S. (01) A ompat assoiation of partile filtering and kernel based objet traking. Pattern Reognition, 45(7): Vu T. D., Burlet J. and Aard O. (011) Grid-based loalization and loal mapping with moving objet detetion and traking. Information Fusion, 1(1): Karasulu B. and Korukoglu S.(01) Moving objet detetion and traking b using annealed bakground subtration method in videos: Performane optimization. Expert Sstems with Appliations, 39(1):