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Kernel based similarity estimation and real time tracking of moving

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  • 1. International Journal of Electronics and Communication Engineering & Technology (IJECET), INTERNATIONAL JOURNAL OF ELECTRONICS AND ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) ISSN 0976 – 6464(Print) ISSN 0976 – 6472(Online) Special Issue (November, 2013), pp. 293-300 © IAEME: www.iaeme.com/ijecet.asp Journal Impact Factor (2013): 5.8896 (Calculated by GISI) www.jifactor.com IJECET ©IAEME Kernel based Similarity Estimation and Real Time Tracking of Moving Objects Manoj Pandey1, J S Ubhi2, Kota Solomon Raju3 1Department of ECE, BKBIET, Pilani, Rajasthan, India of ECE, SLIET, Longowal, Sangrur, Punjab, India 3 Digital System Group, CSIR-CEERI, Pilani, Rajasthan, India 1,2Department 1manoj2pandey@yahoo.com ABSTRACT: In this paper, a traditional Mean Shift algorithm is simulated for tracking a moving object. Kernel based mean-shift algorithm is used for real and non real time tracking and it is observed at various moving constraints such as uniformly moving, fast moving, moving with scale change and moving in overlapping of similar objects. The object is initialized in first frame as a candidate when it first appears in video. The algorithm finds the maximum correlation between target and candidate with kernel based density estimation by similarity function of Bhattacharya co-efficient. The shape of object is used as an ellipse. A kernel based object tracking uses fixed bandwidth which limits the performance when the object scale exceeds the size of tracking window. Therefore in simulation target windows are made adaptive with feature matching. KEYWORDS: Object tracking, Mean Shift, Kernel, Bhattacharya Coefficient. I. INTRODUCTION Object tracking is one of the most popular areas of computer vision. In last two decade object recognition and its tracking become very popular because of its applicability to daily problems and ease of production e.g surveillance cameras, adaptive traffic lights with object tracking and plane detection etc. Objects are represented based on its shape and appearance models explained by Alper et. al in a survey of object tracking [1]. The model selected to represent object shape limits the type of motion or deformation it can undergo. In general, visual tracking algorithms are classified into two categories specified as bottom-up and top-down approach [2]. Filtering and data association [3] is a top down approach while target representation and localization is a bottom-up approach [4-7]. Traditionally, in object tracking implementations feature trackers are used, which gets image sequences, and detect motion after applying the algorithms. Small windows, called features, with certain attributes are selected and then attempts are made to find them in the next frame. Models selected to represent object shape limits the type of motion or deformation it can undergo. International Conference on Communication Systems (ICCS-2013) B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India October 18-20, 2013 Page 293
  • 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME Object tracking are used for applications includes: pattern generation, clustering, face recognition, motion analysis or tracking. Numerous approaches have been dedicated to track non-rigid objects using Kernel based mean shift [4], Mean Shift [5-7] and optical flow [8] method. Comparison of other algorithms used in the application of object tracking and recognition are shown in table 1. These algorithms are implemented either on pure General purpose Processors (GPPs) [9-10] or Field Programmable Gate Arrays (FPGAs) based embedded architectures [11-12]. Mean shift [4-7] is one of the most popular algorithms in object tracking applications used independently or with combination of other filtering techniques. In this paper, a traditional mean Shift algorithm is used in the tracking of two similar moving objects based on Kernel selection. If the similar objects are overlapped in any frame, the tracker shifts from first object to the second after finding good similarities. The tracking object is assumed as shape of ellipse and similarity density function is calculated with the help of Bhattacharya coefficient between target and candidate window. Implementation of algorithm is shown in section II, Results and discussion are presented in section III and paper is concluded finally in section IV. Algorithm used Kernel based Mean Shift[4] Mean shift[5-7] Adaptive Block matching[14] PCA + ICA[3] Nonlinear Kalman filtering[15] Mean shift with Particle filter[16] Eigen Tracking[17] Edge detector [18] II. Feature Selection based on Advantages/Limitations Fixed point Good tracking for uniform, single and slow moving objects mode seeking , clustering, Shadow, Good for Cluster analysis and Kernel, Gaussian Kernel global optimization Using object contour, motion vector, Computationally superior, good in object boundary compression Basis image, generalization of PCA Better face recognition DTA + EGP + Unscented Kalman filter Fast operation Means shift for gradient descend and Particle filter fornon gauss and non linear View based representation, Eigen space representation, articulated objects, rigid, affine, image motion Deformed object Tracking in video images Useful for view point and changes in pose and Robust matching Accurate with occlusion and spurious edges Table 1: Comparison of object tracking algorithms KERNAL BASED MEAN ALGORITHM Mean shift is a non parametric statistical method which was first introduced by Fukunaga in 1975[13], later it is used and explored by D Comaniciu [4-5] for object tracking applications. To characterize the target, first a feature space is chosen. Then reference target model is represented by its probability density function (PDF) q in the feature space. Similarly, a candidate model is represented with PDF function p. A similarity density is calculated between the target model and candidate model to match the maximum similarity with the help of Bhattacharya coefficient ρ [p(x), q]. For example, the reference model can be chosen to be the color PDF of the target. Without loss of generality, the target model can be considered as centered at the spatial location 0. In the subsequent frame, a target candidate is defined at location y, and is characterized by the PDF p(y). Both PDFs are to be estimated from the data. International Conference on Communication Systems (ICCS-2013) B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India October 18-20, 2013 Page 294
  • 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME To satisfy the low-computational cost imposed by real-time processing discrete densities, i.e., m-bin histograms are used. A. Target Model First we need to initialize position and size of search window in first frame. The probability of the feature u = 1, 2 ...m in the target model is then computed as in equation (1) ݊ ‫ݍ‬௨ ൌ ‫ ܥ‬෍ ݇||‫ߜ 2|| ݅ݔ‬ሾܾሺ‫ ݅ݔ‬ሻ െ ‫ݑ‬ሿ … … .1 ݅ൌ1 Kernel profile k weights contribution by distance to centroid and is the Kronecker delta function i.e ࢑ሺ࢞ሻ ൌ ൌ૙ ૚ ሺࢊ ൅ ૛ሻ ‫ כ‬ሺ૚ െ ԡ࢞ԡ૛ ሻ ૛࡯ࢊ ܑ܎ԡ࢞ԡ ൏ 1 ‫܍ܛܑܟܚ܍ܐܜ۽‬ Where Cd is the volume of a unit sphere in an equal number of dimensions to the histogram, x is the distance between normalized pixel location (xi) and the center of the kernel(y), and d is a given constant of 2. In order to apply a mean shift calculation, the set of histogram values is weighted by Epanechnikov kernel to yield a smoothed set of values. it defines an ellipsoidal region and gives more weights to pixel closer to the center of the kernel. This is useful because pixels farther from the center of the object that those pixels are least reliable, The rationale for using a kernel to assign smaller weights to pixels farther from the centre is that those pixels are the least reliable, since they are the ones most affected by occlusion or interference from the background. A kernel with Epanechnikov profile was essential for the derivation of the smooth similarity function between the distributions, since Its derivative is constant; thus the kernel masking lead to a function suitable for gradient optimization, which gave us the direction of the target’s movement. The search for the matching target candidate in that case is restricted to a much smaller area and therefore it is much faster than the exhaustive search. A. Candidate model Let {xi} be the normalized pixel locations of the target candidate, centred at y in the current frame. The normalization is inherited from the frame containing the target model. Using the same kernel profile k(x), but with bandwidth h, the probability of the feature u =1, 2 . . . m in the target candidate is given by equation (2). ௡ ଶ ‫ ݕ‬െ ‫ݔ‬௜ ‫݌‬௨ ሺ‫ݕ‬ሻ ൌ ‫ܥ‬௛ ෍ ݇ ൭ቤቚ ቚቤ ൱ ߜሾܾሺ ‫ݔ‬௜ ሻ െ ‫ݑ‬ሿ. … .2 ݄ ௜ୀଵ A. Similarity Function Now a similarity functions between p and q plays the role of likelihood and its local maxima in the image indicate the presence of objects in the second frame having representations similar to q defined in the first frame as below in equation 3 and 4. International Conference on Communication Systems (ICCS-2013) B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India October 18-20, 2013 Page 295
  • 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME ݀ሺ‫ݕ‬ሻ ൌ ඥ1 െ ߩሾ‫݌‬ሺ‫ݕ‬ሻ, ‫ݍ‬ሿ........3 ߩሺ‫ݕ‬ሻ ൌ ߩሾ‫݌‬ሺ‫ݕ‬ሻ, ‫ ݍ‬ሿ ൌ ∑௠ ඥ‫݌‬௨ ሺ‫ݕ‬ሻ, ‫ݍ‬௨ ……….4 Ԧ Ԧ Ԧ ௨ୀଵ The mode of this density in the local neighbourhood is the sought maximum which can be found employing the mean shift procedure. In this procedure the kernel is recursively moved from the current location to the new location according to the relation shown in equation (5). Apply mean shift: Compute new location Z as zൌ ౯ష౯౟ మ ቚ| ሻ୷୧ h ౯ష౯౟ మ ………… 5 ∑౤h ሺ୵୧୥|ቚ ቚ| ሻ ౟సభ h ∑౤h ሺ୵୧୥|ቚ ౟సభ Where g(x) = -k’(x), assuming that the derivative of k(x) exists for all except for a finite set of points and weight of pixel values are calculated as in equation (6). ௠ ‫ݍ‬௨ ‫ݓ‬௜ ൌ ෍ ඨ ߜሾܾሺ‫ݔ‬௜ ሻ െ ‫ݑ‬ሿ … … … 6 ‫݌‬௨ ሺ‫ݕ‬଴ ሻ ௨ୀଵ A. Implementation Steps The software based design flow of algorithm is well shown by flow chart in figure 1. Each function is shown with red cooled. III. RESULTS AND DISCUSSION In the implementation of the tracker, RGB colour spaces are used as a feature space, in which feature space is quantized into 5 x 5 x 5 bins. The set of histogram values is weighted by Epanechnikov kernel which yields a smoothed set of values. In the calculation of the kernel profile the value of Cd = 2 and d=1is taken. The algorithm runs comfortably at 24 frames per second (FPS) on 2.70 GHz PC, Matlab (version 7.60) as an implementation. The tracking results of the mean shift algorithm are shown in figure 2, from left to right: tracking before overlapping of two similar objects, in middle overlapping of both similar objects and last tracker shifts to another similar object. The distance of target is calculated between frames 320 to 550 with respect bin variations. Graph in figure 3, shows the metric distance between the frames 320 to 550 which is very less compare to frames 450 to 470. It signifies the maximum similarity (i.e. minimum distance) between the target window and the candidate window in the successive frames from 320 to 340. The movement of the object in these consecutive frames is slow in respect to frames from 450 to 470. The change in the histogram affects the similarity (i.e. distance) between the target model and the candidate model. Tracking of object coming towards the camera is used to check the scaling effect as shown in figure 3 from left to right frames (50, 65 and 80). In frame 80 tracker moves out of object which shows that this implementation cannot handle a change of scale from the object. In first case, the size of the ellipse surrounding the object was fixed. Since the orientations of the ellipse are only vertical or horizontal but to get movement in the Z axis difficult to follow in this case. For example an object from the background and advancing toward the camera which International Conference on Communication Systems (ICCS-2013) B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India October 18-20, 2013 Page 296
  • 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME means that size will increase due to which it is hard to monitor the object effectively in 2 dimensional. To track object in such case a scale change function is required to incorporate in the algorithm. Tracking is observed in real time video streaming are shown in figure 4 with the variation in speed of moving objects. Tracker follow the object very well once it is moving with slow and uniform speed but it fails to track the objects for fast and non-uniform speeds. Fig. 1: Flow chart of kernel based Mean Shift algorithm International Conference on Communication Systems (ICCS-2013) B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India October 18-20, 2013 Page 297
  • 6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME Fig. 2: Tracking of a moving person from left to right: in left figure, in middle overlapping of similar object, at bottom tracker shifts to other similar object after overlapping. Fig. 3: The minimum value of distance function of the frame index Fig. 4: Tracking of object coming towards the frame (scale change): from left to right (frames 50, 65 and 80) Fig. 5: Real Time Tracking International Conference on Communication Systems (ICCS-2013) B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India October 18-20, 2013 Page 298
  • 7. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME IV. CONCLUSION The results of Kernel based mean shift algorithm is found to be smoothly tracking a single object if there is a good separation between the two or more than two objects. But if the objects are found similar and overlapped then simple mean shift may track a wrong object. So this shortcoming may overcome by using the filters or with the combination of other algorithms. REFERENCES [1] Alper Yilmaz, Omar Javed, Mubarak Shah, “Object Tracking: A Survey”. ACM Computing Surveys, Vol. 38, No. 4, Article 13, December 2006. [2] Usman Ali, M.B. Malik and Khalid Munawar, hardware/software co-design of a real-time kernel based tracking system, in journal of systems architecture, Volume 56, Issue 8, August 2010, pp. 317–326 [3] Y. Bar-Shalom, Tracking and Data Association, Academic Press Professional,Inc., San Diego, CA, 1987 [4] D Comaniciu, Visvnathan Ramesh, Peter Meer, “Kernel-Based Object Tracking”, IEEE Trans Pattern Anal. Mach.Intell, 25(5):564-577. [5] Comaniciu, D., Ramesh, V., Meer, P., “Real-time tracking of non-rigid objects using mean shift, in: Proc. IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head, vol. 2, pp. 142--149 (2000) [6] Yizong Cheng, “Mean Shift, Mode Seeking, and Clustering”, IEEE Transaction on pattern analysis and machine intelligence, Vol. 17, No. 8, August 1995. [7] D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE Trans. Pattern Anal. Machine Intel. vol. 24, no. 5, pp. 603–619, Dec. 2002. [8] M. Correia, A. Campilho, “Real-time implementation of an optical flow algorithm”, Proc. ICIP, Vol. 4, pp. 247-250, 2002 [9] Manoj Pandey, Dorothi Borgohain, Gargi Baruah, J S Ubhi and Kota Solomon Raju, Real Time Object Tracking: Simulation and Implementation on FPGA based Soft Processor, Proc. QSHINE 2013, Book Chapter, QSHINE 2013, LNICST 115, Springer, pp. 441–450, 2013. [10] Manoj Pandey, Dorothi Borgohain, J S Ubhi and Kota Solomon Raju, Real Time Histogram Computation in Kernel based Tracking System, in Proc. ICAES, pp. 171–174, IEEE Computer Society 2013 [11] Usman Ali, M.B. Malik and Khalid Munawar, FPGA/Soft- Processor based real-time object tracking system, in: Proceedings of the IEEE, 5th Southern Programmable Logic Conference, 2009, pp. 33-37. [12] Usman Ali, M.B. Malik and Khalid Munawar, hardware/software co-design of a real-time kernel based tracking system, in journal of systems architecture, Volume 56, Issue 8, August 2010, pp. 317–326. [13] K. Fukunaga, L.D. Hostetler, “The Estimation of the Gradient of a Density Function, with applications in Pattern Recognition”, IEEE Transactions on Information Theory, January 1975, Vol. 21, pp. 32-40 [14] Karthik Hariharakrishnan and Dan Schonfeld, “Fast object tracking using adaptive block matching”, IEEE Transaction on multimedia, Vol. 7, No. 5, October 2005. [15] Constantinos Antoniou, Member, IEEE, Moshe Ben-Akiva, and Haris N. Koutsopoulos, “Nonlinear Kalman Filtering Algorithms for On-Line Calibration of Dynamic Traffic Assignment Models”, IEEE transactions on Intelligent Transportation Systems, vol. 8, no. 4, December 2007, pp 661-670 International Conference on Communication Systems (ICCS-2013) B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India October 18-20, 2013 Page 299
  • 8. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN 0976 – 6464(Print), ISSN 0976 – 6472(Online), Special Issue (November, 2013), © IAEME [16] Bai and Weiming Liu, “Improved Object Tracking with Particle Filter and Mean Shift”, Proceedings of the IEEE International Conference on Automation and Logistics, August 18 21, 2007, Jinan, China. [17] Michael J. Black and Allan D. Jepson “Eigen Tracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation” European Conf. on Computer Vision, ECCV’96, Cambridge, England, April 1996 [18] Michael A. Greminger and Bradley J. Nelson “A Deformable Object Tracking Algorithm Robust to Occlusions and Spurious Edges”, Proceedings of the 2005 IEEE International Conference on Robotics and Automation Barcelona, Spain, April 2005 BIOGRAPHY Manoj Pandey (Corresponding Author) was born in Sidharthnagar (U.P.), India in October 10, 1982. He did his M.Sc in Electronics from Deen Dayal Upadhyay Gorakhpur University, Gorakhpur, UP, India in 2005 and received M.Tech in Electronics Design and Technology from Tezpur University (Central), Tezpur in year 2007. At present he is working as an Assistant Professor at B K Birla Institute of Engineering and Technology Pilani and pursuing PhD from Sant Longowal Institute of Engineering & Technology, Longowal Punjab in area of FPGA based Reconfigurable Architectures for Image Processing applications. The co-authors of this paper are supervisor for his PhD thesis. International Conference on Communication Systems (ICCS-2013) B K Birla Institute of Engineering & Technology (BKBIET), Pilani, India October 18-20, 2013 Page 300