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Object tracking by dtcwt feature vectors 2-3-4


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Object tracking by dtcwt feature vectors 2-3-4

  1. 1. INTERNATIONALComputer Engineering and Technology ENGINEERING International Journal of JOURNAL OF COMPUTER (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME & TECHNOLOGY (IJCET)ISSN 0976 – 6367(Print)ISSN 0976 – 6375(Online) IJCETVolume 4, Issue 2, March – April (2013), pp. 73-78© IAEME: Impact Factor (2013): 6.1302 (Calculated by GISI) © OBJECT TRACKING BY DTCWT FEATURE VECTORS V Purandhar Reddy1 1 Associate Professor, Department of ECE, S V College of Engineering, Tirupati, India ABSTRACT In this paper, a novel algorithm for object tracking in video pictures based on Dual Tree Complex Wavelet Transform Feature Vectors has proposed. The position vector of an object in first frame of a video has been extracted based on selection of ROI. Region of Interest (ROI) is a cropped image in a first frame. In this algorithm, object motion has shown in nine different directions based on the position vector in the first frame. We extract nine position vectors for nine different directions. With these position vectors second frame is cropped into nine blocks. We exploit block matching of the first frame with nine blocks of the next frame using DTCWT transform feature vectors. The matched block is considered as tracked block and its position vector is a reference location for the next successive frame. We describe algorithm in detail to perform simulation experiments of object tracking which verifies the tracking algorithm efficiency. Keywords: Block Matching, DTCWT, Distance Measure, Feature Vector, Tracking Algorithm. 1. INTRODUCTION The moving object tracking in video pictures has attracted a great deal of interest in computer vision. For object recognition, navigation systems and surveillance systems, object tracking is an indispensable first-step. The conventional approach to object tracking is based on the difference between the current image and the background image. However, algorithms based on the difference image cannot simultaneously detect still objects. Furthermore, they cannot be applied to the case of a moving camera. Algorithms including the camera motion information have been proposed previously, but, they still contain problems in separating the information from the background. 73
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 09766367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME In this paper, we propose block matching based Method for object tracking in videopictures. Our algorithm is based on position vector calculation, Feature Vectors by DTCWT andblock matching .The proposed method for tracking uses block matching between successive frames.As a consequence, the algorithm can simultaneously track multiple moving and still objects invideo pictures. This paper is organized as follows. The proposed method consisting of stages positionvector calculation, feature extraction, block matching and minimum distance measure which are alculation,described in detail.2. POSITION VECTOR CALCULATION In general, image segmentation and object extraction methods are used to calculateposition vectors. In the proposed concept, first select the portion of an object which is to be concept,tracked. The portion of an image is cropped in the first frame which is referred as block ie shownin the figure (1). Based on co-ordinate parameters of an object, we extract the position of the pi ordinate pixel Pxmax(Pxmin) which has the maximum (minimum) x-component x Pxmax = (Xmax,x,Xmax,y), Pxmin = (Xmin,x,Xmin,y), Where Xmax,x, Xmax,y, Xmin,x, and Xmin,y are x and y coordinates of theRightmost and leftmost boundary of the object i, respectively. In addition, we also extract nd Pymax = (Ymax,x, Ymax,y), Pymin = (Ymin,x, Ymin,y).Then we calculate the width w and the height h of the objects as follows wi(t) = Xmax,x − Xmin,x, hi(t) = Ymax,y − Ymin,y. We define the positions of each object in the frame as follows fol P = (X1,Y1) X1(t) = (X max,x + X min,x)/2 Y1(t) = (Ymax,y + Ymin,y)/2 Fig.1: Explanation of the position vector calculation from the cropped image result 74
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEMEFrame to Frame movement distance of an object is negligible. So, we consider movementshift by “m” units in nine different directions.For Direction 1, position vector shift P1= (X1-m,Y1) For Direction 2, position vector shift P2= (X1+m,Y1) For Direction 3, position vector shift P3= (X1,Y1+m) For Direction 4, position vector shift P4= (X1,Y1-m) For Direction 5, position vector shift P5= (X1-m,Y1-m) For Direction 6, position vector shift P6= (X1+m,Y1+m) For Direction 7, position vector shift P7= (X1-m,Y1+m) For Direction 8, position vector shift P8= (X1+m,Y1-m) For Direction 9, position vector shift P9= (X1,Y1) Based on the position vectors P1, P2, P3, P4, P5, P6, P7, P8 and P9 crop the secondframe into nine blocks.3. FEATURE EXTRACTION FOR BLOCKS BY DTCWT Dual Tree Complex Wavelet Transform is a recent enhancement technique to theDiscrete Wavelet Transform with some additional properties and changes. It is a effectivemethod for implementing an analytical wavelet transform.DTCWT gives the complex transform of a signal using two separate DWT decompositionsie., tree ‘a’ and tree ‘b’. DTCWT produces complex coefficients by using a dual tree ofwavelet filters and gives real and imaginary parts which are shown in Fig(2). Fig.2: Real and imaginary parts of the complex coefficients 75
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME The DTCWT has following properties: • Approximate shift invariance; • Good directional selectivity in 2-dimensions (2-D) with Gabor-like filters also true for higher dimensionality: m-D); • Perfect reconstruction (PR) using short linear-phase filters; • Limited redundancy: independent of the number of scales: 2:1 for 1-D ( 2m :1 for m-D) • Efficient order-N computation - only. • DTCWT differentiates positive and negative frequencies and generates six sub bands oriented in ±15°, ±45°, and ±75 °. The different levels of DTCWT such as levels 5, 6, and 7 are applied on Cropped Blocks. In this paper we have used DTCWT Coefficients for feature vector calculation. Forfirst frame the feature vector is f1 based on cropped image using position vector P1.For nextframe feature vector set is V={V1,V2,V3,V4,V5,V6,V7,V8,V9} based on position vectorsP1,P2,P3,P4,P5,P6,P7,P8 &P9.4. BLOCK MATCHING AND DISTANCE MEASURE Proposed algorithm for object tracking exploit block matching with the DWT featuresabove and make use of minimum distance search in the feature space. We now go into moredetails of our algorithm. Using the cropped images result of the object in the tth frame, we first extract theDWT coefficients of the cropped image (N+1,i). Here the notation N+1,i stands for thecropped image in the tth frame. Then we perform the minimum distance search between(N+1,i) and (N,j) for all cropped images j in the next frame using position vectors. Finally thecropped image N+1,i is identified with the cropped image in the next frame which hasminimum distance from N+1,i. Repeating this matching procedure for all the frames withfirst frame, we can identify all blocks one by one and can keep track of the blocks betweenframes. 4.1 OBJECT TRACKING ALGORITHM 1. Input video 2. Crop the first frame for ROI(Region Of Interest) 3. Calculate position vector for cropped portion. P=(X1,Y1) 4. Based on position vector calculate nine position vectors with X and Y co-ordinate shift by ‘m’ units. P1= (X1-m,Y1), P2= (X1+m,Y1), P3=(X1,Y1+m),P4=(X1,Y1-m), P5= (X1-m,Y1-m),P6=(X1+m,Y1+m), P7= (X1-m,Y1+m), P8= (X1+m,Y1-m), P9= (X1,Y1) 5. For previous frame i.e first frame perform DWT for cropped block to get feature vector f1. 6. Similarly perform DTCWT for all cropped blocks in the next frame N+1 to get the feature vectors V1 ,V2, V3, V4, V5, V6, V7 ,V8, V9 . V= { V1, V2, V3, V4, V5, V6, V7, V8, V9} 76
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME 7. BLOCK MATCHING DISTANCE MEASURE a.) Calculate the distance measured using Manhattan distance between f1 and V. We get distance set of D = {D1, D2, D3, D4, D5, D6, D7, D8, D9} where D1= Distance between f1 and V1 similarly for D2,D3,D4,D5,D6,D7,D8 and D9 are calculated. b.) Apply block matching of Nth frame cropped image with minimum distance block of N+1th frame. If not matched, perform for next successive frame. c.) After matching remove the position vector data of Nth frame and store the data of position vector of N+1th frame. d.) Increase the value of N by N+1. 8. Repeat the steps from 1 to 7.5. SIMULATION RESULTS The proposed algorithm is tested by using Matlab 7.1. For experimental verificationstwo different video sequences were taken from moving camera, then frames were extractedfrom the video sequences. Since, all the processing has done on colour images, 24 bit colorimage first frame has been initially taken as reference frame to calculate position vector. Bygivingframes one after the other to the Matlab program of proposed algorithm, the trackedobject location is extracted. Fig.3: The tracked object results from successive frames6. CONCLUSION We have proposed an object tracking algorithm for video pictures, based on positionvectors and block matching of the extracted objects between frames in a simple feature space.Simulation results for frame sequences with moving objects verify the suitability of thealgorithm for reliable moving object tracking. We also have confirmed that the algorithmworks very well for more complicated video pictures including rotating objects and occlusionof objects. It is obvious that, the simulation result in the proposed algorithm is quite enoughto apply for the real time applications. We would like to implement this algorithm withfeature vectors in different vectors for future applications too. 77
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME7. REFERENCES[1] Object Tracking based on pattern matching V. Purandhar Reddy, International Journalof Advanced Research in “ Computer Science and Software Engineering “, Volume 2,Issue 2, February 2012.[2] Object Tracking based on Image Segmentation and pattern matching V. Purandhar Reddy,International Journal of Computer Science and Information technology, Issue 1,volume-26,May 2011.[3] Object Tracking based on Image Segmentation and pattern matching, V. PurandharReddy, GJCEA issue-1, Feb 2012.[4] H. Kimura and T. Shibata, “Simple-architecture motion-detection analog V-chip based onquasi-two-dimensional processing,” Ext. Abs. of the 2002 Int. Conf. on Solid State Devicesand Materials (SSDM2002), pp. 240– 241, 2002.[5] John Eakins and Margaret Graham.Content-based image retrieval.Project ReportUniversity of Northumbria at Newcastle. [6] Datta 2005. Content-based image retrieval approaches and trends of the new age.In Proceedings of the 7th ACM SIGMM International Workshop on Multimediainformation Retrieval. Singapore: ACM Press. pp. 253-262.[7] Gao Li-chun and Xu Ye-qiang. 2011. Image retrieval based on relevance feedback usingblocks weighted dominant colors in MPEG-7. Journal of Computer Applications.vol.31(6),pp.15491551.[8] S.Vidivelli and S.Sathiyadevi. 2011. Wavelet based integrated color image retrieval.IEEE-International Conference on Recent Trends in Information Technology, ICRTIT.[9] Dr. Prashant Chatur and Pushpanjali Chouragade, “Visual Rerank: A Soft ComputingApproach for Image Retrieval from Large Scale Image Database” International journal ofComputer Engineering & Technology (IJCET), Volume 3, Issue 3, 2012, pp. 446 - 458,ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.[10] K.Ganapathi Babu, A.Komali, V.Satish Kumar and A.S.K.Ratnam, “An Overview ofContent Based Image Retrieval Software Systems” International journal of ComputerEngineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 424 - 432, ISSN Print:0976 – 6367, ISSN Online: 0976 – 6375. 78