MOVING OBJECT TRACKING IN VIDEO SCENES ON EMBEDDED LINUX AND BEAGLEBOARD-XM A.SWETHA 1 M.S.SHEEBA 2 1,2

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1 MOVING OBJECT TRACKING IN VIDEO SCENES ON EMBEDDED LINUX AND BEAGLEBOARD-XM A.SWETHA 1 M.S.SHEEBA 2 1,2 Department of E.C.E, Sathyabama University, Jeppiar nagar, Chennai, Tamil nadu. 1 swethabalaji1990@gmail.com, 2 shedar.ms@gmail.com 764 ABSTRACT Video tracking in real time is one of the most important topic in the field of computer Vision. To detect and track moving objects in the video scenes is the first relevant step in the information extraction in many computer vision applications. This design can be used for the surveillance purpose, video annotation, traffic monitoring and also in the field of medical In this paper, we will discuss about the different methods for the video tracking using Opencv software and the implementation of the tracking system on the Beagle board XM. Background Subtraction method and Haar Transform are the different methods using for the tracking. And finally, we concluded that the background subtraction method is most efficient method for tracking all the moving objects in the frames. Keywords: Surveillance, OpenCV, Background Subtraction, Haar Transform [1] INTRODUCTION Video tracking is the process of segmenting a particular area of interest from the different frames and tracking its movement. It can also be defined as the constantly moving dynamic entities under the influence of different factors. The factors can be illumination, change of the light, change of the appearance, scale, rapid association of the object etc. The changes in the one of these factors can influence the results of the object tracking. The idea of video tracking will be used in the areas of vehicle navigation, video surveillance, automobile driver assistance, robotics, video games, biometrics and in the field of medical sciences. During the last few years, various researches has been undergoing for the different algorithms used for the tracking the particular segment. Meanshift method, Camshift method, Kalman filter, Background Subtraction methods are the different methods used for the tracking of the objects in the video scenes. Meanshift method is an iterative method, by computing the meanshift value of the current position of the object and then moves the point to its meanshift value as the new position. The camshift method used to track the object is the modification of the meanshift method. The only difference is in the size of the search window. In the meanshift method, the size of the search window is fixed, but in the camshift method the size of the search window can be adjusted as per the movement of the object. Kalman filter can also be as the predictor- corrector method. It will estimate the position of the object in each frame of the sequence. Kalman filter can reduce the noise in the tracking due to the illumination, the change of the light etc. Color based tracking is by taking the HSV value of the particular color tracking the value in the frames and the background subtraction method is also a efficient and easy method to find all the moving objects in the frame. [1.1] OBJECT TRACKING Video tracking is the process of locating a moving object (or multiple objects) over time using a camera. It has a range of uses, some of which are: human-computer relations, security and surveillance, video communication and compression, augmented certainty, traffic control, medical imaging and video cutting. Video tracking can be a time overriding process due to the amount of data that is enclosed in video. Adding further to the complexity is the possible need to use object recognition techniques to track a challenging problem in its own right. The objective of video tracking is to associate target objects in consecutive video frames. The relationship can be especially difficult when the objects are moving fast relative to the frame rate. Another situation to increase the complexity of the problem is when the tracked object changes direction over time. For these situations video tracking systems usually employ a motion model which describes how the image of the target might change for different possible motions of the object. Video trackers rely on an internal representation of target appearance, the appearance model, which is compared to measurements extracted from incoming frames at candidate target positions to estimate the most likely target location. To create the appearance model and the measurements, trackers project image regions at candidate target positions onto lower dimensionality feature spaces that highlight relevant information for the tracking task.early trackers kept the appearance model fixed throughout a sequence, while focusing on robust localization and matching strategies. Recently, several methods have been proposed to track targets while evolving the appearance model in order to adapt it to changing imaging conditions. Appearance model adaptation introduces several challenges, such as the need for simultaneous fulfillment of the contradicting goals of rapid learning and stable memory. This is referred to as the stability/plasticity dilemma. Adaptive trackers also incur the risk of including wrong measurements while updating the target model, thus leading to drifts from the target. To avoid drifting, model adaptation has to be robust to outliers generated by the appearance of the background or other objects that may partially occlude the

2 765 target. Finally, an adaptive tracker must be able to evaluate on line (without ground truth) the quality of the estimated target location, so as to adjust accordingly its contribution to model update. These challenges, together with the different solutions adopted to address them, make the evaluation and the choice of an adaptive tracker a difficult task. The goal of the object tracking is to track the exact position of the object in the next frame by frame. First, we tried to implemented the system using the MATLAB software, but its very slow to process the video and the other drawback of MATLAB software is that it can work only with recorded video. Thus to ensure the progress of the work, we used the Opencv software in Angstrom 2.2, and the object tracking system implemented on the Beagleboard XM, which is a low price embedded Linux based board that can be supported the Windows CE, Linux, Symbian OS. Video camera used is a normal USB webcam which have a 4.0 MP video resolution at 15 fps.beagle board xm is specifically designed for the open source community. Memory of the hardware is accessed by using a μsd card. Fig.1. Experimental setup with Beagleboard and web camera [2] EXISTING SYSTEM The goal of the object tracking is to track the exact position of the object in the next frame by frame. First, we tried to implemented the system using the MATLAB software, but its very slow to process the video. And the other drawback of MATLAB software is that it can work only with recorded video. The drawbacks are as given below: Speed: Matlab is build on Java, and Java is built upon C. So when you run a Matlab program, your computer is busy trying to take all that Matlab code. Then it will turn into Java, and then finally executes the code. OpenCV, on the other hand, is fundamentally a library of functions written in C/C++.. So ultimately you get more image processing done for computers processing cycles, and not more interpreting. As a result of this, program written in OpenCV run much faster than similar programs written in Matlab. So OpenCV is fast when it comes to speed of execution. For example, we may write a small program to detect peoples smiles in a sequence of video frames. In Matlab, we would usually get 3-4 frames analysed per second. In OpenCV, we will get at least 30 frames per second, resulting in real-time detection. Resources needed: Due to the high level nature of Matlab, it use a lot of your systems resources. Matlab code requires over a gig of RAM to run through video. In comparison, distinctive OpenCV programs only require 70mb of RAM to run in real-time. Cost: List price for the base (no toolboxes) MATLAB (commercial, single user License) is around USD OpenCV (BSD license is free. Portability: MATLAB and OpenCV run equally well on Windows, Linux and MacOS. However, when it comes to OpenCV, any device that can run C, can, in all probability, run OpenCV. It can support only Intel Processors. [3] PROPOSED SYSTEM In this we apply Python Opencv software in Ubuntu OS to track the moving objects in video scenes. Then the object tracking system is implemented on the Beagleboard XM, which is a low cost embedded Linux based board that can support Windows CE, Linux, Symbian OS. Video camera used is a normal USB webcam which have a 4.0 MP video resolution at 15 fps. Beagle board xm is particularly designed for the open source community. Memory of the hardware is accessed by μsd card. A. Background Subtraction Method Background subtraction method is the efficient method to track the objects in real time. This method is able to track all the moving objects in the video frames. In this method, the background is kept as stable. The block diagram of the proposed method is given in fig 2. Adaptive thresholding is used to split the foreground and background. Background subtraction method probabilistically model the background pixel color. This method will calculate the probability of an arbitrary pixel of the background based on the color. If the probability is greater than the threshold value then the pixel is considered as the background. Otherwise, it is considered as the moving object s pixels. If the background pixels are known, then the next step is to subtract the current position of the image from the background. From the incoming video frames, the pixels must be smoothening

3 766 using the Gaussian filter. N and N+1 Th frame should be subtracted and the difference frame is converted to gray scale image. The resulting Gray scale frame must be thresholded. After the erosion and dilation operation,we use the contour tracking to track all the moving objects in the frame. Along with the tracking of the moving objects, it is able to detect the direction of the movement. Python software needs to download the numpy module which supports the array. If the lens movement is greater than zero and the numpy array value is in between -30 and -1, then it will detect the LEFT direction and display as LEFT. Comparing to the other algorithms, this is the most efficient and easiest way. A robust background subtraction algorithm must be able to handle lighting changes, repetitive motions from clutter and long-term scene changes.the following analyses make use of the function of V(x,y,t) as a video sequence where t is the time dimension, x and y are the pixel location variables. e.g. V(1,2,3) is the pixel intensity at (1,2) pixel location of the image at t = 3 in the video sequence. The background is assumed to be the frame at time t. This difference image would only show some intensity for the pixel locations which have changed in the two frames. Though we have apparently removed the background, this approach will only work for cases where all foreground pixels are moving and all background pixels are static. Fig 2: Background Subtraction method B. HAAR TRANSFORM Object Detection using Haar feature-based cascade classifiers is an effective thing. It is a machine learning based approach where a cascade function is trained with a lot of positive and negative images. It is then used to detect objects in other images. Now all possible sizes and locations of each kernel is used to calculate plenty of features. Even a 24x24 window results over features). For each feature calculation, we need to find sum of pixels under white and black rectangles. To solve this, we introduced the integral images. It simplifies calculation of sum of pixels, how large may be the number of pixels, to an operation involved with four pixels. Each image is given an equal weight in the beginning. After each classification, weights of misclassified images is increased. Then again same process is done. Error rates and new weights are calculated. The process is continued until required accuracy or error rate is achieved or required number of features are found).final classifier is a weighted sum of these weak classifiers. It is called weak because it alone can t classify the image, but together with others forms a strong classifier.in Haar Transform we will do the detection and tracking with the help of Cascade classifiers. There are two applications in Opencv to train cascade classifier they are as given below opencv_haartraining and opencv_traincascade..opencv_traincascade is the new version which is written in C++ in accordance to OpenCV 2x API.Main difference between this two is that opencv_traincascade is used for both Haar and Local Binary Patterns.(LBP). LBP features are integers in contrast to Haar features,so training and detection with LBP features are several times faster than that of Haar features. These methods train classifiers in different file formats. [3] EXPERIMENTAL RESULTS We implemented different algorithms for the tracking using Python opencv software in Ubuntu platform. The whole object tracking system is implemented on the Beagle board xm. Background Subtraction method is

4 767 the easiest and simplest method to track all the moving objects in the frame. The camera used should be kept as stable. Otherwise, due to the movement of the camera, it may detect the objects without any movement. The results with background subtraction method are shown in the fig3.1. Fig 3.1: Moving Objects are detected using the background Subtraction method in different frames. We have loaded the xml files of many objects in OpenCV for the purpose of detection of Haar trainings and the results are as below Fig 3.2: Detection of Features with Haar Transform The results of the comparision are as follows when we go with Haar Transform it is very efficient and it is suitable for short distances and time taken to detect will be more when compared with Background Subtraction and if we want to track longer distances and for faster we can go with Background. [4] CONCLUSION AND FUTURE SCOPE This paper deals on the simultaneous tracking of the multiple objects in real time. During the comparison test, a range of pre-processing methods are applied to the frame pixels. The tracking of the various objects and colors are different from each other in different methods. From this discussion we can conclude that the Background subtraction method is superior to other algorithms. This method can detect all the moving objects by keeping the background stable. The contour based color tracking method and also the available camshift, Kalman filter methods are capable of tracking only single objects. The whole object tracking system is easily implemented on the Beagle board xm. In the future, we will implement the object tracking system and its comparison work on the Panda board ES, which is the advanced version of the Beagle board xm. ACKNOWLEDGMENT We would like to thank Dr.G.Sundari Head of the Department, Department of Electronics and Communication Engineering, Sathyabama University, Ms. M.S. Sheeba, Assistant.Professor, Department of Electronics and Communication Engineering,Sathyabama university for her encouragement and support. REFERENCES [1]. Afef SALHI and Ameni YENGUI JAMMOUSSI, Object tracking system using Camshift, Meanshift and Kalman filter, World Academy of Science, Engineering and Technology, 2012

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