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### 649 652

1. 1. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 Multiple Target Tracking with the help of Mean Shift Algorithm Mahesh Kumar Chouhan1, Rahul Mishra2, Dr. Dhiiraj Nitnawwre3 Department of Electronics Engineering Institute of Engineering & Technology D.A.V.V., Indore-452001, India Abstract―The multi moving targets tracking are a typical In multi target tacking, the general problem is to change injob in the field of visual surveillance. The main difficulties in appearance due to some complex situations such as noises,targets tracking are fast motion of the target, suddenly clutters, occlusions, fast object motions and suddenlyvelocity variations, clutters, complex object shapes and changes in velocity, etc. The mean shift algorithm is aocclusions, etc. Target tracking are basically explain in to kernel region based tracking, in other words it is a type ofthree main parts: point tracking, kernel tracking andsilhouette tracking. Here we can track multiple targets with kernel region tracking [2], [3]. Normally tracking isthe help of mean shift algorithm. For tracking purpose, the defined in two parts: detect and track. First part detectsmean shift method requires a kernel that mean’s a circle or a means to find the location of the objects and then secondrectangle or an ellipse region. First with the help of mean part mean’s to follow the movement of objects inshift, we can evaluate the objects locations. In mean shift consecutive video sequences.algorithm the Bhattacharyya coefficient show an importanttask for tracking. It is a similarity function which can be In these algorithm we tracks targets region and it is showobtained by target model and target candidate. The with the help of histogram. A similarity function that isexperimental results describe that the mean shift algorithm is defined on the detail of target model and target candidate, itpractical for tracking point of view. is use to obtain the value of Bhattacharyya coefficient and Index Terms―Target Tracking, Mean Shift Method, Target it is also known as Bhattacharyya function [2]. The meanRepresentation & Localization, Bhattacharyya Coefficient. shift algorithm described by using the target representation procedure [5]. On the first side, the mean shift is a simple method which provides normal and reliable results of I. INTRODUCTION targets tracking. On the other side mean shift lost the targets when the targets are moving fast or occlusions Multi target tracking is an interesting but typical task. occurs.The main requirement of target tracking is first to detectthe target and then track this target. Several methods are When the objects with small displacement then theavailable for targets tracking but it is basically explain in to performance of mean shift algorithm are good but it is notthree main parts: point tracking, kernel tracking and guaranteed if the objects with large displacement or fastsilhouette tracking [1]. In point tracking, the targets are motion or occlusion then its performance are good. So thedenoted with the help of points. For targets tracking the objective of targets tracking is to tracks target objectskernel tracking use an ellipse or a rectangle or a circle position in consecutive frame sequences. The mean shiftregion[2], [3]. In silhouette tracking, the targets are denoted method is a nonparametric density estimator and it isby the boundary regions [1]. calculates the nearest mode of a sample distribution [3], [4], [5]. The mean shift method is so popular because of its Targets tracking are required in many applications such implementation and real time response.as traffic monitoring, vehicle navigation, automatedsurveillance and human computer interaction, etc [1]. The whole work is organized as follows. The section II explains the detail of mean shift algorithm. Experimental Manuscript received May, 2012. results detail shows in section III. Conclusions are Mahesh Kumar Chouhan, Department of Electronics Engineering,Institute of Engineering & Technology D.A.V.V., Indore-452001, India, described in last section IV.(e-mail: kumar.mahesh45@yahoo.com).Rahul Mishra, Department of Electronics Engineering, Institute of II. DETAILS OF MEAN SHIFT ALGORITHMEngineering & Technology D.A.V.V., Indore-452001, India, (e-mail:rmishra_sati@yahoo.com). A. Mean shiftDr. Dhiiraj Nitnawwre, Department of Electronics Engineering, Instituteof Engineering & Technology D.A.V.V., Indore-452001, India, (e-mail: The mean shift method has an iterative procedure fordhiirajnitnitnawwre@gmail.com). targets tracking. It is a nonparametric density estimator to 649 All Rights Reserved © 2012 IJARCET
2. 2. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012finds the nearest mode of a sample distribution. The mean (3)shift algorithm is used to optimize local maxima of theprobability density function (pdf) [3], [4], [5]. The mainfeature of this Algorithm is histogram optimization, by this whereoptimization to find the direction and location of themoving objects. We can compare the histograms of theobjects in current frame and next frame of images. (4) B. Target Representation D. Target Localization The target representation characterize the targets and it isconsists by target model and target candidate [2], [5]. If you want to track the location of object exactly in the image, the distance d(y) should be minimized and it is Target model: the targets are represented by circular equivalent to maximize the Bhattacharyya coefficient.regions in image sequences. Assume that all the targets are Apply linear Taylor expansion in equation (4).initially normalized to a unit circle. This target model isfounded by its colour histogram of m bins (pdf). Thehistogram only considers the pixels inside a rectangle or anellipse region. * Let {xi }i 1...n be the normalized pixel locations in the (5)target model, which is centered at origin (0). So theprobability of the feature u=1....m in the target model isobtained as where (1) (6) where is the target model, is the probability of the element, is normalization constant and is the Let in the current frame the new target location starts atKronecker delta function. the point location. At every step of the iterative mean shift algorithm the obtained target shifts from to and Target Candidate: similarly the target candidate and its it is defined aspdf is approximated by a colour histogram of m bins. Let {xi }i 1...n be the normalized pixel locations in thetarget candidate, which is centered at y location in the next (7)image. So the probability of the feature u=1....m in thetarget candidate is obtained as where g(x) = - (x) and k(x) is kernel function. The (2) whole mean shift algorithm is in short as follows. where is the target candidate, is the 1: Basic Algorithm for Mean Shiftprobability of the element, is normalization For multi target tracking, generate the target modelsconstant. by equation (1) and its positions C. Bhattacharyya Coefficient from the previous frame. Bhattacharyya coefficient is a similarity function that is 1. Initialize the centers of the ellipses in the currentused to calculate the similarity between the target model frame at , then calculate the targetand target candidate [2]. The Bhattacharyya coefficientvaries with locations within images. candidates by equation (2) and evaluate Bhattacharyya coefficients by equation (4) . 650 All Rights Reserved © 2012 IJARCET
3. 3. ISSN: 2278 – 1323 International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 4, June 2012 2. Drive the weights i.e. by equation (6). 3. Calculate the next positions of the target candidates by equation (7). Figure 1: multiple object tracking by mean shift 4. Calculate the target candidates Figure 2 shows the Bhattacharyya coefficient of object1 by equation (2) and evaluate Bhattacharyya and object2. coefficient by equation (4) at next positions, 5. If and Stop. Bhattacharyya Coefficient for Object1 Bhattacharyya Coefficient 1 Else set & and repeat from 0.9 step 2. 0.8 0.7 0 10 20 30 40 50 60 70 80 90 100 Frame Number III. RESULTS AND DISCUSSION Bhattacharyya Coefficient for Object2 Bhattacharyya Coefficient 1 Here we performed an example and computes the results 0.9by the mean shift method. This example tested on Matlab 0.8software. The detail of this image sequences are jpg images 0.7with 320x240 resolutions per frame. The ability of themean shift method is shows by this example. The image 0 10 20 30 40 50 60 70 80 90 100 Frame Numbersequences “MULTIPLE” has total number of frames are 91frames for tracking. Figure 2: Bhattacharyya coefficient of object1 and object2 Frames 8, 12, 30 and 42 shows the results at the normal Figure 3 shows the horizontal and vertical positions ofcondition means without large displacement or fast motionor occlusions and it is show in figure1. object1 and object2.Means here we seen that the movement Frame 8 Frame 34 of object1 & object 2 in graphically. Horizontal Position 300 object1 object2 200 Pixels 100 0 0 10 20 30 40 50 60 70 80 90 100 Frame Number Vertical Position 110 object1 100 object2 Pixels 90 80 Frame 66 Frame 91 70 0 10 20 30 40 50 60 70 80 90 100 Frame Number Figure 3: horizontal and vertical positions of object1 and object2 So the finally results show that the mean shift algorithm is better for small displacement. 651 All Rights Reserved © 2012 IJARCET