Detection ad Tracking of Multiple Moving Objects in Video Sequence using Entropy Mask Method and Matrix Scan Method

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1 Proc. of Int. Conf. on Multimedia Processing, Communication& Info. Tech., MPCIT Detection ad Tracking of Multiple Moving Objects in Video Sequence using Entropy Mask Method and Matrix Scan Method Dhanesha R, 1 and Anandakumar S P, 2 1 Asst. Professor, Department of Computer Science & Engineering, 2 Department of Computer Science & Engineering, Adichunchanagiri Institute of Technology. Chikmaglur, Karnataka, India. dhanesh025@gmail.com Abstract --The security of restricted areas such as borders is of utmost importance, in particular with the worldwide increase of military conflicts, illegal immigrants, and terrorism over the past decade. Monitoring such areas rely currently on technology and man power, however automatic monitoring has been advancing in order to avoid potential human errors that can be caused by different reasons. This paper introduces automatic multiple moving object detection and tracking system which uses image processing and implementation integrates the entropy difference method and matrix scan method. We apply the entropy difference detection method to detect the coarse region of the moving objects in the image and we have constructed the mask covering a detected coarse region. The detected multiple moving object in video sequence available in form of Clausius image. Second, taking the initial value of the level set for moving object as the constructed mask region, and we using the matrix scan method to track the detected multiple moving object in the video sequence, objects are tracked by drawing a border around the detected moving objects. Experiment results and snapshots provided demonstrate that our method can detect and track effectively and accurately the motion objects in video sequence. Keywords:- entropy method; matrix scan method; Clausius image; detection and tracking I. INTRODUCTION In recent years, with the latest technological advancements, off-the-shelf cameras became vastly available, producing a huge amount of content that can be used in various application areas. Among them, visual surveillance receives a great deal of interest nowadays. Until recently, video surveillance was mainly a concern only for military or large-scale companies. However, increasing crime rate, especially in metropolitan cities, necessitates taking better precautions in security-sensitive areas, like country borders, airports or government offices. Moving object detection is a basic and important problem in video analysis and vision applications. Automatic tracking systems for still and moving objects can be invalid in security applications, such as monitoring border areas, buffer zones and restricted areas. A simple tracking system would comprise a camera fixed high above the monitored zone, where images of the zone are captured and consequently processed. Processing the captured images can be in three phases, namely, detection of a moving object, tracking of the object and finally recognition of the object. Frame difference method [1], optical flow [3] and entropy mask [2] have been used for detecting moving objects in image sequences. For example, adaptive optical flow [4] for person tracking is dependent on being able to locate a person accurately across a series of DOI: 03.AETS Association of Computer Electronics and Electrical Engineers, 2013

2 frames. Optical flow can be used to segment a moving object from a scene, provided the expected velocity of the moving object is known; but successful detection also relies on being able to segment the background. However, the high computational time to extract the optical flow and the lack of discrimination of the foreground from the background, make this method unsuitable for real time processing. On the other hand, entropy mask method detects moving objects by comparing the entropies of the images sensitive to illumination changes and small movement in the images, e.g. leaves of trees. In this paper, we presented new detecting and tracking technique of moving objects that combines the entropy mask method and the matrix scan method. Our algorithm is comprised of four steps. First, we have introduced the concept of Clausius entropy and then we transform the gray value domain of observed image into Clausius entropy domain. Secondly, we prepare the temporal mask image representing the coarse region, and then we use the entropy difference method to detect the coarse region of moving objects from a complex image. Third, using the initial value of moving object as the extracted mask image, we have applied the matrix scan to track rapidly and exactly the boundary of moving objects. Fourth, we recognize the detected moving objects and give the alarming in real-time for dangerous object. II. DETECTION ALGORITHM OF MOVING OBJECTS The detection of moving objects in video sequence is divided into two steps. The first one transform gray scale image into Clausius entropy image. The second one find the initial coarse region of moving objects. A. Computation Of Clausius Entropy Image All Rudolf Julius Emanuel Clausius who was a German physicist and mathematician introduced a mathematical version of the concept of entropy in 1865 [6]. Entropy S is not defined directly, but rather by an equation relating the change in entropy of the system to the change in heat of the system. For a constant temperature, the change in entropy, ΔS, is defined by the equation S=ΔQT where ΔQ is the amount of heat absorbed in an isothermal and reversible process in which the system goes from one state to another, and T is the absolute temperature at which the process is occurring. If the temperature of the system is not constant, then this relationship is represented by a differential equation ds=dqt To understand what this equation means, suppose that the temperature T can be expressed as a function T(Q) of the heat Q. Then the total change in entropy as the heat-level varies is S=A 1T(Q)dQ where A is the set defining the range of heat values in the system. We need to define three items that are the system or field F, the energy or heat Q, and temperature T [7] in order to compute the Clausius entropy for pixel value in each frame of image sequence. First, we define the system as an input video sequence I composed of gray-scale of color images I t. Each pixel in frame image I t of video has a w w rectangular neighborhood or window. Here, each pixel consisting the windows of every frame takes the energy from former input image and emits the energy to next output image. Second, we consider the energy of given frame. We can define the absorbed energy from input image I t as follows Qk(t)=l:all of pixel in windowwk(xklt-mklt)2 where X kl is the color value of k th channel at the l th pixel in window, M kl (t) is the mean of all color values of the k th channel for pixels, and w k is a weight function for each channel. Moreover, in order to make a system to be adapted with time, we have to update the mean value M kl (t) for each frame image I (t). It is adjusted as follows Mkl(t)=1-λMklt-1+λ Xkl(t) where λ is the learning factor for adapting current means. In general, the larger difference of temperature between two objects causes the greater movement of energy. In similarly, we have defined that the amount of energy is increase according to the difference between color value and mean value is more and more large. 66

3 Third, we define the absolute temperature of system. On the microscopic scale, the temperature can be defined as the average energy of the each particle in the system. Hence, if we take the proportional constant in relation between heat and temperature as κ, then the change of temperature in thermodynamic area can be defined as follows T=K Qn where n is the total number of particles belonging to some object. Here, we can define the temperature having the variety in each frame as T(t)=1-ρTkt-1+K Qn where ρ is the constant proportional to the amount of loss heat in every frame. The temperature is defined on above satisfies the rule of heat system which is the larger difference of temperature between two objects causes the greater movement of energy. Moreover, the amount of emission energy in each frame may be given as QL=σn TK Fourth, we compute the total quantity of entropy variation at each pixel (x, y) in the t th frame by taking the summation of entropy variation in each channel ΔStx,y=k:channel Skt(x,y) The Clausius Entropy method is based on frame change of entropy which attempts to detect moving regions by making use of the sum of entropy variation for consecutive frames in a video sequence. It is seemed that this method is highly adaptive to static environment. So this is good for detecting the motion areas. B. Coarse Detection Of Moving Object We have used the background subtraction method to detect quickly the coarse region of multiple moving objects. Background subtraction is based on frame difference between first frame and current input frame, which attempts to detect moving regions in a video sequence. This method is highly adaptive to static environments. So background subtraction method is good at providing initial coarse motion area. Let I 1 (x) and I t (x) represent the Clausius entropy value at pixel position x and at time instance 1 and t of video image sequence I which is in the range [0,255]. In order to detect cases of slow motion or temporally stopped objects, an adaptive threshold value is used to compute the entropy difference image DI t (x) as shown in the following equations: DIt(x)=1 if It x-i1x > Td(x)0 otherwise Where T d (x) is a threshold value estimated using the distribution of entropy difference values. Finally, the procedure that we use to detect the coarse region of moving objects is described as follows: 1) Prepare the tracking mask image for representing region of moving objects. 2) Compute the difference entropy value of two consecutive entropy images in image sequence. 3) Compare between the absolute of difference entropy value and threshold T d, and if this value is greater than T d, assign one to pixel value for its position in the temporal tracking mask image, otherwise, assign zero to pixel value for its position. 4) Morphological operations such as erosion and dilation are applied to the coarse detection region in order to remove noise or restore some missing pixels of moving objects. 5) And then we use the coarse detection mask image as an initial value of fast level set technique to extract smooth and extract boundary of moving objects. The process of the coarse detection is shown in Figure.1. III. TRACKING ALGORITHM OF MOVING OBJECT Here, we propose the active region-based tracking algorithm based on a matrix scan method (MSM). It reduces considerably the computational complexity and improves the real-time performance. Furthermore, it 67

is doing a real-time updating the exact matching boundaries of multiple moving objects in the tracking process to solve the object missing problem when objects have large deformation (a) (b) Fig.1.

//Psuedocode of matrix scan method for tracking of moving objects Input: Detected frame C, and corresponding frame from input video B. Output: Frame contains border for moving objects.

4 is doing a real-time updating the exact matching boundaries of multiple moving objects in the tracking process to solve the object missing problem when objects have large deformation (a) (b) Fig.1. The process of coarse detection: (a) The frame of the image Sequence, (b) The Clausius entropy image of (a), (c) Detected Mask image The following psuedocode illustrates the matrix scan method; //Psuedocode of matrix scan method for tracking of moving objects Input: Detected frame C, and corresponding frame from input video B. Output: Frame contains border for moving objects. Method: for (i 1 to number of rows) do for (j 1 to number of columns) do if ( C i,j 0) then (c) if ( C i-1,j 0) then set red color to pixel B i-1,j if ( C i,j-1 0) then set red color to pixel B i,j-1 if ( C i,j+1 0) then set red color to pixel B i,j+1 68

if ( C i+1,j 0) then set red color to pixel B i+1,j end for end for Algorithm ends IV. EXPERIMENTAL RESULTS The proposed method was tested using real video sequences.

The experimented input image with the Clausius Image and the Course Detected image with various frames can easily be deduced at further levels for proper understanding.

5 if ( C i+1,j 0) then set red color to pixel B i+1,j end for end for Algorithm ends IV. EXPERIMENTAL RESULTS The proposed method was tested using real video sequences. We have investigated the performance of our method as follows. Figure.2 show the experimental results for the video sequence. The experimented input image with the Clausius Image and the Course Detected image with various frames can easily be deduced at further levels for proper understanding. (a) Input image (b) Clausius image (c) Course Detected Image (d) 81 st Frame (e) 119 th Frame (f)156 th Frame (g) 188 th Frame (h) 224 th Frame (i) 249 th Frame Fig.2. Experimental results in video: (a) 174 th input image, (b) Clausius entropy dense image, (c) Coarse detected image, (d)-(i) Tracking results by the matrix scan method. 69

6 V. CONCLUSIONS Hence by, the proposed system presents an application with automatic detection and extraction techniques. Any moving object in a video stream is successfully detected and extracted specifically. By combination of Clausius entropy theory and the Matrix scan method, it is really significant to denote and track the performance of a moving object in a video sequence. Hence, this approach makes the overall system very efficient, scalable, robust and convenient than all other conventional methods used for both indoor and outdoor image sequences. Acknowledgement First and foremost we thank the Almighty for being our light and for showering his gracious blessings throughout the course of this project. We express our gratitude to our beloved principal Dr. C.K. Subbaraya, Adichunchangiri Institute of Technology, Chikmagalur, for his ever patronizing nature and relentless support. We are thankful to all the teaching and non teaching staffs of Department of Computer Science and Engineering and to all those who have directly and indirectly extended their help to us in completing this project work successfully. We extend our gratitude to our family members and our beloved friends, who had been strongly supporting us in all our endeavors. REFERENCES [1] C. Zhan, X. Duan, S. Xu, Z. Song, and M. Luo, An Improved Moving Object Detection Algorithm Based on Frame Difference and Edge Detection, in Proc. IEEE 4th Int. Conf. Image and Graphics, Chengdu,China, Aug. 2007, pp [2] Wanhyun Cho1, Sunworl Kim1, Gukdong Ahn1, Sangcheol Park Detection And Tracking Of Multiple Moving Objects In VideoSequence Using Entropy Mask Method And Matrix Method in Proc. IEEE [3] J. H. Duncan, and T. C. Chou, Temporal edges: The detection ofmotion and the computation of optical flow, in Proc. IEEE 2nd Int.Conf. Computer Vision, Florida, USA, Dec. 1988, pp [4] S. Denman, V. Chandran, and S. Sridharan, Adaptive Optical Flow forperson Tracking, in Proc. Digital Image Computing: Techniques and Applications, Cairns, Australia, Dec. 2005, pp [5] F.-H. Cheng and Y.-L. Chen, Real time multiple objects tracking and identification based on discrete wavelet transform, Pattern Recognition, Vol. 39, pp , [6] P. Pierre, A to Z of Thermodynamics, Oxford University Press, [7] Eunjin Ko et al., Clausisus Normalized Field for motion segmentation, Proceeding of 2009 IPIU conference, Jeju, Korea. (Written by Korea) [8] X. Zhang, R. Zhao, and Z. Ma, A new method for moving object trackingautomatically, in Proc. IEEE Int. Symp.Communications and Information Technology, Beijing, China, vol. 2, Oct. 2005, pp

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