ICTACT Journal on Image and Video Processing

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Tracking of Multiple Human Objects using Combination of Daubechies Complex Wavelet Transform and Zernike Moment


Affiliations
1 Department of Information Communication and Technology, Dhirubhai Ambani Institute of Information and Communication Technology, India
2 Department of Electronics and Communication, University of Allahabad, India
     

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The goal of multi object tracking is to find location of the target objects in number of consecutive frames of a video. Tracking of multiple human objects in a scene is one of the challenging problems in computer vision applications due to illumination variation, object occlusion, abrupt motion etc. This paper introduces a new method for multiple human object tracking by exploiting the properties of Daubechies complex wavelet transform and Zernike moment. The proposed method uses combination of Daubechies complex wavelet transform and Zernike moment as a feature of objects. The motivation behind using combination of these two as a feature of object, because shift invariance and better edge representation properties make Daubechies complex wavelet transform suitable for locating object in consecutive frames whereas translation invariant property of Zernike moment is also helpful for correct object identification in consecutive frames. The proposed method is capable to handle full occlusion, partial occlusion, split and object re-enter problems. The experimental results validate the effectiveness and robustness of the proposed method.

Keywords

Multiple Object Tracking, Daubechies Complex Wavelet Transform, Zernike Moment, Shift-Invariance, Translation Invariance, Occlusion.
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  • A. Yilmaz, O. Javed and M. Shah, “Object Tracking: A Survey”, ACM Computing Surveys, Vol. 38, No. 4, pp. 1-45, 2006.

  • W. Hu and T. Tan, “A Survey on Visual Surveillance of Object Motion and Behavior”, IEEE Transaction on System Man and Cybernetics, Vol. 34, No. 3, pp. 334-352, 2006.

  • L. Wang, W. Hu and T. Tan, “Recent Developments in Human Motion Analysis”, Pattern Recognition, Vol. 36, No. 3, 585-601, 2003.

  • A.S. Jalal and V. Singh, “A Multi-Resolution Framework for Multi-Object Tracking in Daubechies Complex Wavelet domain”, International Journal on Computational Vision and Robotics, Vol. 3, No.1-2, pp. 52-74, 2012.

  • M. Khare, R.K. Srivastava and A. Khare, “Object Tracking using Combination of Daubechies Complex Wavelet Transform and Zernike Moment”, Multimedia Tools and Applications, Vol. 76, No. 1, pp. 1247-1290, 2017.

  • A. Khare and U.S. Tiwary, “Daubechies Complex Wavelet Transform based Moving Object Tracking”, Proceedings of IEEE Symposium on Computational Intelligence in Image and Signal Processing, pp. 36-40, 2007.

  • O. Prakash and A. Khare, “Tracking of Non-Rigid Object in Complex Wavelet Domain”, Journal of Signal and Information Processing, Vol. 2, No. 2, pp. 105-111, 2011.

  • O. Prakash and A. Khare, “Tracking of Moving Object using Energy of Biorthogonal Wavelet Transform”, Chiang Mai Journal of Science, Vol. 42, No. 3, pp. 783-795, 2015.

  • M. Piccardi, “Background Subtraction Technique: A Review”, Proceedings of IEEE International Conference on System, Man and Cybernetics, pp. 3099-3104, 2005.

  • J.C. Huang and W.S. Hsieh, “Wavelet based Moving Object Segmentation”, IET Electronic Letters, Vol. 39, No. 19, pp. 1380-1382, 2003.

  • A. Baradarani, “Moving Object Segmentation using 9/7-10/8 Dual Tree Complex Filter Bank”, Proceedings 19th IEEE International Conference on Pattern Recognition, pp. 1–4, 2008.

  • M. Khare, R.K. Srivastava and A. Khare, “Moving Object Segmentation in Daubechies Complex Wavelet Domain”, Signal Image and Video Processing, Vol. 9, No. 3, pp. 635-650, 2015.

  • M. Khare, R.K. Srivastava and A. Khare, “Single Change Detection-Based Moving Object Segmentation by using Daubechies Complex Wavelet Transform”, IET Image Processing, Vol. 8, No. 6, pp. 331-344, 2014.

  • Y.P. Guan, “Spatio-Temporal Motion-Based Foreground Segmentation and Shadow Suppression”, IET Computer Vision, Vol. 4, No. 1, pp. 50-60, 2010.

  • M. Khare, R.K. Srivastava and A. Khare, “Daubechies Complex Wavelet based Computer Vision Applications”, Proceedings of International Conference on Recent Developments in Biometrics and Video Processing Techniques, pp. 138-155, 2013.

  • M. Khare, R.K. Srivastava and A. Khare, “Dual Tree Complex Wavelet Transform based Shadow Detection and Removal from Moving Objects”, Proceedings of International Conference on Electronic Imaging, pp. 1-7, 2014.

  • M. Khare, R.K. Srivastava and A. Khare, “Moving Shadow Detection and Removal-A Wavelet Transform based Approach”, IET Computer Vision, Vol. 8, No. 6, pp. 701-717, 2014.

  • A. Mittal and L. Davis, “M2tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene”, International Journal of Computer Vision, Vol. 51, No. 3, pp. 189-203, 2003.

  • K. Smith, D.G. Perez and J.M. Odobez, “Using Particles to Track Varying Numbers of Interacting People”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 962-969, 2005.

  • P. Nillius, J. Sullivan and S. Carlsson, “Multi Target Tracking-Linking Identities using Bayesian Network Inference”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 2187-2194, 2006.

  • M. Han, W. Xu, H. Tao and Y. Gong, “Multi-Object Trajectory Tracking”, Machine Vision and Applications, Vol. 18, No. 3, pp. 221-232, 2007.

  • R. Rad and M. Jamzad, “Real Time Classification and Tracking of Multiple Vehicles in Highways”, Pattern Recognition Letters, Vol. 26, No. 10, pp. 1597-1607, 2005.

  • A. Amer, “Voting based Simultaneous Tracking of Multiple Video Objects”, IEEE Transactions on Circuit Systems for Video Technology, Vol. 15, No. 11, pp. 1448-1462, 2005.

  • F.H. Cheng and Y.L. Chen, “Real Time Multiple Objects Tracking and Identification based on Discrete Wavelet Transform”, Pattern Recognition, Vol. 39, No. 6, pp. 1126-1139, 2006.

  • A. Senior, A. Hampapur, Y.L. Brown, S. Pankanti and R. Bolle, “Appearance Models for Occlusion Handling”, Journal of Image and Vision Computing, Vol. 24, No. 11, pp. 1233-1243, 2006.

  • K.P. Soman, K.L. Ramachandran and N.G. Resmi, “Insight into Wavelets-From Theory to Practice”, Prentice Hall, 2010.

  • I. Daubechies, “Ten Lecture on Wavelets”, SIAM, 1992.

  • D. Clonda, J.M. Lina and B. Goulard, “Complex Daubechies Wavelets: Properties and Statistical Image Modelling”, Signal Processing, Vol. 84, No. 1, pp. 1-23, 2004.

  • M. Teague, “Image Analysis via the General Theory of Moments”, Journal of Optical Society of America, Vol. 70, No. 8, pp. 920-930, 1980.

  • S.K. Hwang and Y.Y. Kim, “A Novel Approach to the Fast Computation of Zernike Moment”, Pattern Recognition, Vol. 39, No. 11, pp. 2065-2076, 2006.

  • G.A. Papakostas, Y.S. Boutalis, D.A. Karras and B.G. Mertzios, “A New Class of Zernike Moments for Computer Vision Applications”, Information Sciences, Vol. 177, No. 13, pp. 2802-2819, 2007.

  • Y. Gurwicz, R. Yehezkel and R.B. Lachover, “Multiclass Object Classification for Real Time Video Surveillance Systems”, Pattern Recognition Letters, Vol. 32, No. 6, pp. 805-815, 2011.

  • A. Khotanzed and Y.H. Hong, “Invariant Image Recognition by Zernike Moment”, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 12, No. 5, pp. 489-497, 1990.

  • E.M. Celebi and Y.A. Aslandogan, “A Comparative Study of Three Moment based Shape Descriptors”, Proceedings of International Conference on Information Technology: Coding and Computing, pp. 788-793, 2005.

  • Y. Bin and P.J. Xiong, “Invariance Analysis of Improved Zernike Moments”, Journal of Optics A: Pure and Applied Optics, Vol. 4, No. 6, pp. 606-614, 2002.

  • M. Vorobyov, “Shape Classification Using Zernike Moments”, Technical Report, iCamp at University of California, 2011.

  • M. Khare, R.K. Srivastava, A. Khare, N.T. Binh and T.A. Dien, “Human Object Classification in Daubechies Complex Wavelet Domain”, Proceedings of 2nd International Conference on Context Aware System and Applications, pp. 125-132, 2013

  • INRIA Person dataset, Available at: http://pascal.inrialpes.fr/data/human, Accessed at 2017.

  • D. Comaniciu, V. Ramesh and P. Meer, “Kernel based Object Tracking”, IEEE Transactions of Pattern Recognition and Machine Intelligence, Vol. 25, No. 5, pp. 564-575, 2003.


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  • Tracking of Multiple Human Objects using Combination of Daubechies Complex Wavelet Transform and Zernike Moment

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Authors

Manish Khare
Department of Information Communication and Technology, Dhirubhai Ambani Institute of Information and Communication Technology, India
Ashish Khare
Department of Electronics and Communication, University of Allahabad, India

Abstract


The goal of multi object tracking is to find location of the target objects in number of consecutive frames of a video. Tracking of multiple human objects in a scene is one of the challenging problems in computer vision applications due to illumination variation, object occlusion, abrupt motion etc. This paper introduces a new method for multiple human object tracking by exploiting the properties of Daubechies complex wavelet transform and Zernike moment. The proposed method uses combination of Daubechies complex wavelet transform and Zernike moment as a feature of objects. The motivation behind using combination of these two as a feature of object, because shift invariance and better edge representation properties make Daubechies complex wavelet transform suitable for locating object in consecutive frames whereas translation invariant property of Zernike moment is also helpful for correct object identification in consecutive frames. The proposed method is capable to handle full occlusion, partial occlusion, split and object re-enter problems. The experimental results validate the effectiveness and robustness of the proposed method.

Keywords


Multiple Object Tracking, Daubechies Complex Wavelet Transform, Zernike Moment, Shift-Invariance, Translation Invariance, Occlusion.

References