International Conference on Avance Electronic Science an Technolog (AEST 016) Multi-camera tracking algorithm stu base on information fusion a Guoqiang Wang, Shangfu Li an Xue Wen School of Electronic Engineering, Heilongjiang Universit, Harbin 150000, China. Abstract. Intelligent vieo surveillance technolog is a part of pattern recognition use for analzing, etracting an recognizing behavior characteristics of a moving target basing on computer algorithms. The target tracking algorithm combines particle filter an Mean-shift in overlapping multi-camera environment on the intelligent vieo surveillance sstem. Multicamera tracking is stuie using a fusion of SURF characteristics, color characteristics an geometric characteristic in Matlab. Eperimental results show that the metho of tracking between multiple cameras has goo accurac an stabilit. Kewors: characteristics fusion; target tracking; peestrian matching. 1 Introuction Intelligent vieo surveillance technolog is one aspect of pattern recognition, which uses a number of computer algorithms [1], analsis, etraction an recognition of moving objects within the vieo behavioral characteristics, an we use the computers to etermine whether the behavioral characteristics of the moving object are efine as "suspicious behavior". During the fift ears of research an evelopment, the target tracking has been largel use in various areas []. One camera can etect limite area, it is ifficult to track target with multiple perspectives, an long time an wie range, so using multiple cameras become inevitable. The avantages of multi-camera tracking are the wie fiel of view, comprehensive perspective an large monitoring area, which makes the multicamera tracking become popular. Metho summar Ever feature of object has its limitations when we match the object, so it is unable to reflect the characteristics of the target onl b using one feature, an it is also ifficult to match a target. Thus the robustness woul be improve if we use a various tpe of features. This paper uses SURF, color an geometric characteristics of peestrian profile to complete matching [3]. This metho is base on etecting, we shoul work out the SURF ke escriptor, color an geometric characteristics of the profile which are locate in the ivie target area, an then we calculate the size of the istance between the feature matries, as a result of those above, we coul complete the peestrian matching. The basic process of this paper shows below [4]. First of all, in orer to hanle the vieo sequences a Corresponing author : wangguoqiang@hlju.eu.cn 016. The authors - Publishe b Atlantis Press 50
from ifferent cameras, we shoul use backgroun subtraction to finish the motion segmentation an obtain the binar image of the target. The result is showe in Figure 1. Figure 1. The results of motion segmentation. 3 Feature etractions 3.1 SURF feature etraction SURF feature points matching metho is an improve metho of SIFT, mainl in terms of spee, an its efficienc is higher than the SIFT. SURF algorithm processes integral images an convolution that is onl relate to previous image, the own sampling is epening on applie increasing size of its core [5]. SURF algorithm can be analze at the same time several laers of image scale space, an eliminating the nee for two image sampling process, so the eecution time of the algorithm can be improve. SURF algorithm can be ivie into the following four steps: (1) Describe Gaussian prami structure scale space; () Locate feature points Position; (3) Determine the irection of the feature point; (4) Calculate SURF escriptors. (1) Describe Gaussian prami structure scale space The most important point of the reuce running time for SURF algorithm is that it uses the integral image. The formula (1) is given: i< j< I (1) = I ( ) (, ), i= 0 j= 0 The using filter is ifferent between SURF an SIFT when the buil the multi-scale space, SURF uses bo filter which is shown in Figure. Figure. Bo filter. SURF algorithm uses the Hessian matri eterminant approimation image. Formula () is the Hessian matri of one image piel: 51
H( f(, )) f f f f = () That is, each piel can orer out a Hessian matri. But because the feature point scale nee to have scale irrelevance, we nee to eecute Gaussian filter first, an then buil Hessian matri. The formula is given: L ( X, δ) L ( X, δ) H( X, δ ) = (, δ) (, δ) L X L X (3) δ Where, L ( X, δ) = G( δ)* I( X ), G( δ ) =, δ is the variance. L (, ) X δ is the image of a point of secon-orer filter with Gaussian convolution. L ( X, δ ), L ( X, δ ) is similar as above. Using mean filter to solve the secon erivative of the Gaussian in SURF, the approimation coefficients is w = 0.9, an then Hessian matri can be get b the formula above, the formula is: et( ) (0.9 ) H = D D D (4) () Locate feature points Position The principle of positioning feature points accuratel is that we shoul abanon all values which are lower than the setting etreme value, so that the number of iscoverable feature points can be reuce an ultimatel we can receive some points which have the most obvious features. (3) Determine the irection of the feature point After etecting the feature points, we nee to etermine the main irection of the feature point, to ensure that the feature points have a characteristic of rotation an scale invariance. We shoul select a ranom area corresponing to the regional scale in the vicinit of the points, an ientif the main orientation. SURF selects a circle area, an etermines the main orientation b using a metho of activit sector, the main orientation of a line can be realize. (4) Calculate SURF escriptors The metho of getting SURF escriptors: Select a 0s size of winow along the main orientation of the feature point position, an ivie it equall into a 4 4 rectangle, then calculate the Haar wavelet response of the feature points in each 5 5 rectangle space separatel, we can see that in Figure 3, is the Haar wavelet response of orientation, is the Haar wavelet response of orientation. Firstl, we shoul set a Gaussian-weighte for an intereste points. ( δ = 3.3 areas,, an in the vicinit of the center of s ) Then, we shoul calculate the cumulative sum of each rectangle. After this, each rectangle area has a 4-imentional intensit which represents a vector, its vector can be escribe as V s (,,, ), an finall we can get a 64-imentional feature vector. It can improve the robustness of the geometric istortion an positioning errors. = 5
Figure 3. The ke escriptors constitute. The similarit between the two images is etermine b using the Eucliean istance of the ke feature vector in SURF algorithm [6]. We take a critical point within the first image an search the former two feature points in the secon image which has a minimum Eucliean istance with the critical point. If the value that the minimum istance ivies the secon-smallest istance is lower than the set threshol value, we amit the two matche points [7]. 3. Color feature etraction Color feature has an important role in most object tracking methos because of its avantages, for eample, it has the invariance. It is changeless with object s changing imensions as well as rotatio n. Firstl, we translate the target image from the RGB color space to HSV color space. Then, after getting SURF ke point coorinates (,, ) we shoul calculate the H, S an V mean value of a 3 3 neighborhoo of the ke point. Finall, the piel values of ke point (, ) have been translate from ( RGB,, ) space into HSV space an escribe as the mean values (h,s,v), which is 3D color feature vector V c =[h, s, v]. 3.3 Geometric characteristics of the target profile The geometric characteristics of the peestrian s profile are escribe b the contour of the peestrian an the positional relationship of curve. In that case, we can etract several special points through the peestrian contour ege, calculate Eucliean istance between SURF feature points an etracte points, an efine the istance as the geometric characteristics of the peestrian profile. We use Cann operator to acquire the ege of the peestrian profile image, then use the results of binar image segmentation to remove the backgroun image of peestrian area an use a black backgroun to replace the original backgroun, so we ensure basicall that the Cann ege etraction operator is integrate, which is shown in Figure 4. Figure 4. Geometric features an its contour map. Select several special points from the ege of the image outline to etract the geometrical features of a peestrian (shown in Figure 4): (1) The maimum istance of the peestrian outline is efine as the long ais AD, which 53
represents the height of a peestrian profile; () Accoring to AD, we efine a horizontal ais BC which is perpenicular to the longituinal ais AD an place it at 1/4 AD, which represents the with of the peestrian contour; (3) Assume an SURF ke point P an the istance from P to A, D, B, C which constitutes the object contour feature vector V p : PA PD PB PC V p =,,, (5) AD AD AD AD Because the various features of V p is performe b calculating the ratio, V p possesses the invariance propert when the image is in a conition of rotation, translation an scale change. Detecte feature vectors have the same feature point in an peestrian image, an each feature point is a stable feature escriptor, which consists of the following parts: SURF escriptor V represents local S features characteristic points, color vector V c represents the color characteristics, geometric vector V p can remove local similar feature. So each point escriptor vector can be epresse as: F = αv, β, (1 α β) s Vc V p (6) In formula (6), α an β are weight an balance features, we aopt α = 0.3, β = 0.4. 4 Target tracking base on fusion We suppose that at t moment, there are M targets in the fiel of Camera i, which are segmente an marke; there are N targets in the fiel of Camera j, which are segmente an marke, M an N perhaps be equal. i We efine the - peestrian in Camera i is O, the number of SURF ke points obtaine is a ; j the - peestrian in Camera j is O, the number of SURF ke points obtaine is b ; calculate V c an V p which are in the 3*3 vicinit of the SURF ke points at the same time an get the feature vector matri value [ a *135] an [ b *135]. Give two feature escriptors F i an F i, then D can be got b the formula (7): where, 63 3 α β α β i j sik sjk ij pik pjk k= 0 k= 0 D = F F = ( V V ) + + (1 ) ( V V ) (7) ij = ( h h ) + ( s s ) 1 1 1 1 h + s + h + s 1 1 1 1 (8) The similarit of two matche is given: ( D 1 1, D,..., D ) DO (, O)= a a (9) The step of the algorithm is as follows: (1) The quantitative istribution of the whole peestrian areas in Camera i is 1~ M, an the quantitative istribution of the whole peestrian areas in Camera j is 1~ N. 54
() We select a peestrian area p an a multi-feature vector escriptor z in the fiel of Camera i, an calculate the istance D b formula (7) with all the escriptors in the whole peestrian area q of Camera j. Search the minimum similarit istance D between two escriptors an get the similarit istance between p an q b formula (9). (3) Back to step () an stop until we go through all the peestrian area of Camera i. Get the similarit istance between all the peestrian areas of Camera i an Camera j. (4) For an peestrian area in Camera i, the peestrian area in Camera j is matche to the one which has the minimum similarit istance. 5 Eperimental results an analsis Figure 5 is one image of Camera i an j, the image size is piel. There is a smaller overlapping area between Camera 1 an Camera. There is onl one peestrian in the fiel of Camera. We calculate the istance between the peestrian in Camera an the three peestrians in Camera 1 b formula (7) an (9) respectivel, an then we compare them an fin that the minimum istance is successfull matche with the camera 1. The match results are shown in Table 1. Table 1. The results of matching. Target in camera 1 1 3 The istance 0.549 0.5359 0.5096 The tracking results are shown in Figure 6, an the operating environment is MATLAB010a, the computer configuration is Pentium (R) Dual-Core processor TL56 3.00GHz, G memor, Winows7 (3-bit) operating sstems. (a) The view of camera 1 (b) The view of camera Figure 5. The image of Camera 1 an Camera. (a) The results of camera 1 (b) The results of camera Figure 6. The results of tracking. 55
6 Summar This paper is in the framework of multi-camera vieo monitoring an analsis sstem, we use a fusion of SURF feature escriptors, colors an the geometric characteristics of the peestrian s profile to match the targets, an aim at improving the accurac of peestrian matching. Further stu will focus on how to link capture information from each camera an how to improve the efficienc of collaboration between multiple cameras in terms of time an accurac. References 1. C. Dorin, Kernel-base object tracking. IEEE Trans on Patern Analsis an Machine Intelligence, 5, 564-577 (013). J. Czz, B. Ristic, B. Macq, A particle filter for joint etection an tracking of color objects. Image Vision Computing, 5, 171-181(01) 3. R.Mazzon, P. A.Cavallaro, Multi-camera tracking using a Multi-Goal Social Force Moel. Neurocomputing, 100, 41-50(013) 4. M. Piccari, E. D. Cheng, Track matching over isjoint camera views base on an incremental major color spectrum histogram. IEEE conference on Avance Vieo an Signal Base Surveillance. Como, 147-15(01) 5. G. Y. Lian, J. H. Lain, W. S. Zheng, Spatial-temporal consistent labeling of tracke peestrians across non-overlapping camera views. Pattern Recognition, 44, 111-1136(010) 6. P. L. Mazzeo, P. Spagnolo, T. D Orazio, Object Tracking b Non-overlapping Distribute Camera Network., 516-57(ACIVS, 01) 7. X. G. Wang, Intelligent multi-camera vieo surveillance: A review. Pattern Recognition Letters, 34, 3-19(013) 56