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Visual Tracking Enhancement of Object on Circular Path based on Tuned Kalman Filter by Particle Swarm Optimization

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Saad Zaghlul Saeed, Muhamad Azhar Abdilatef, Zead Mohammed Yosif
10.5120/ijca2016910685

Saad Zaghlul Saeed, Muhamad Azhar Abdilatef and Zead Mohammed Yosif. Visual Tracking Enhancement of Object on Circular Path based on Tuned Kalman Filter by Particle Swarm Optimization. International Journal of Computer Applications 146(4):43-50, July 2016. BibTeX

@article{10.5120/ijca2016910685,
	author = {Saad Zaghlul Saeed and Muhamad Azhar Abdilatef and Zead Mohammed Yosif},
	title = {Visual Tracking Enhancement of Object on Circular Path based on Tuned Kalman Filter by Particle Swarm Optimization},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2016},
	volume = {146},
	number = {4},
	month = {Jul},
	year = {2016},
	issn = {0975-8887},
	pages = {43-50},
	numpages = {8},
	url = {http://www.ijcaonline.org/archives/volume146/number4/25390-2016910685},
	doi = {10.5120/ijca2016910685},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Digital image presents information in two-dimensional data, which can be used as feedback measurement for robot visual servoing control. Median filter and morphological operation are used for object detection and extraction its features. Kalman filter is applied for visual measurements that contain noises and uncertainties captured by video camera over the time. Sinusoidal Kalman filter and sinusoidal measurement model is used. The derivations of noise’s process and matrices’ control are presented. The Kalman filter is tuned by using PSO optimization to produce values closer to the true spatial measurements of the target. A developed PSO is proposed in which adaptive inertia weight chaotic PSO algorithm and velocity constriction factor are used in order to overcome premature and local optimum convergence. Simulation for tracking object on circular path are presented. Experimental result shows good performance of the proposed method for noisy measurement of the target.

References

  1. Sarp Ertürk, “Digital Image Processing,” February 2003 Edition, University of Kocaeli February 2003 Edition Part Number 323604A-01 National Instruments Corporation.
  2. A. Muis and K. Ohrishi, “Eye-to-Hand Approach on Eye-in-Hand Configuration within Real-Time Visual Servoing,” IEEE/ASME Transactions on Mechatronics, Vol.10, No. 4, 2005.
  3. S. Y. Chen, “Kalman Filter for Robot Vision: A Survey,” IEEE Transactions on Industrial Electronics, Vol. 59, No. 11, November 2012.
  4. B. Torkaman and M. Farrokhi, “A Kalman–Filter Method for Real-Time Visual Tracking of a Moving Object Using Pan and Tilt Platform,” IJSER Journal, Vol. 3, No. 8, August-2012.
  5. R. K. Jatoth and Dr. T. K. Kumar, “Swarm Intelligence Based Tuning of Unscented Kalman Filter for Bearings Only Tracking,” Int. J. of Recent Trends in Engineering and Technology, Vol. 2, No. 5, Nov 2009, DOI: 01.IJRTET.02.05.335.
  6. M. A. Badamchizadeh, N. Nikdel, and M. Kouzehgar, “Optimization of data fusion method based on Kalman filter using Genetic Algorithm and Particle Swarm Optimization,” IEEE 2nd International Conference on Computer and Automation Engineering (ICCAE), Vol. 5, 2010, pp.359-363, DOI: 10.1109/ICCAE.2010.5451413
  7. R. K. Jatoth and T. K. Kumar, “Particle Swarm Optimization Based Tuning of Extended Kalman Filter for Maneuvering Target Tracking,” International Journal Of Circuits, Systems and Signal Processing, Issue 3, Volume 3, pp. 127-136, 2009.
  8. G. Lin, Z. Jing and Z. Liu, “Tuning of extended kalman filter using inproved particle swarm optimization for sensorless control of induction motor,” Journal of Computational Information Systems 10(6):2455-462 · January 2014. DOI: 10.12733/jcis9762
  9. M. E. H. Pedersen and A. J. Chipperfield, “Simplifying Particle Swarm Optimization,” Applied Soft Computing, vol.10, no.2, March 2010, pp.618-628.
  10. John C. Russ, “The image processing handbook,” CRC Press, 6th edition. April 7, 2011. pp. 885
  11. R. C. Gonzalez, “Digital Image Processing,” 3rd edition, Prentice-Hall, Inc., 2007.
  12. R. C. Marsal, “Morphological And Statistical Analysis Of Biomaterials with Applications in Tissue Engineering by Means of Microscopy Image Processing,” IEEE Latin America Transactions, vol. 9, no. 3, 2011, pp. 399-407.
  13. G. Welch and G. Bishop, “An Introduction to the Kalman Filter,” University of North Carolina at Chapel Hill Chapel Hill, July 24, 2006.
  14. P. Zarchan and H. Musoff, “Fundamentals of Kalman Filtering: A Practical Approach,” Third Edition 2009.
  15. S. S. Rao, “Engineering Optimization Theory and Practice,” Fourth Edition, John Wiley & Sons, Inc., 2009, pp. 708.
  16. S. D. Chavan and N. P. Adgokar, “An Overview on Particle Swarm Optimization: Basic Concepts and Modified Variants,” IJSR Journal, Vol. 4, No.5, 2015
  17. A. Alfi, “Particle Swarm Optimization Algorithm with Dynamic Inertia Weight for Online Parameter Identification Applied to Lorenz Chaotic System,” ICIC International journal, Vol.8, No.2, February 2012.
  18. A. Djoewahir, T. Kanya, and M. Shenglin, “A Modified Particle Swarm Optimization with Nonlinear Decreasing Inertia Weight Based PID Controller for Ultrasonic Motor,” International Journal of Innovation and Technology, Vol.3, No.3, June 2012.
  19. R. Malwiya and V. Rai, “Optimal Speed Controlling of Induction Motor Using New PSO,” Journal IJATER, Vol.5, Nol.2, March 2015.
  20. N. M.A. Ibrahim, H. E.M. Atti, H. E.M. Talaat, and A. H. K. Alaboudy, “Modified Particle Swarm Optimization Based Proportional-Derivative Power System Stabilizer", I.J. Intelligent Systems and Applications, Vol. 3, 2015, pp62-76.
  21. D. Tian, “A Review of Convergence Analysis of Particle Swarm Optimization,” International Journal of Grid and Distributed Computing", Vol.6, No.6, 2013.
  22. J.C. Bansal, P. K. Singh, M. Saraswat, A. Verma, S. S. Jadon, and A. Abraham, “Inertia Weight Strategies in Particle Swarm Optimization,” IEEE 3rd World Congress on Nature and Biologically Inspired Computing, Nature and Biologically Inspired Computing, Salamanca 19-21 Oct. 2011, pp.633 – 640, DOI: 10.1109/NaBIC.2011.6089659.
  23. C. H. Yang, S. W. Tsai, L. Y. Chuang, and C. H. Yang, “A Modified Particle Swarm Optimization for Global Optimization,” International Journal of Advancements in Computing Technology, Vol.3, No. 7, August 2011.
  24. S. M. Elsayed, R. A. Sarker, and E. M. Montes, “Particle Swarm Optimizer for constrained optimization,” Evolutionary Computation (CEC), 2013 IEEE Congress on, Year: 2013, Pages: 2703 - 2711, DOI: 10.1109/CEC.2013.6557896 , IEEE Conference Publications.
  25. C. Ratanavilisagul and B. Kruatrachue, “A modified particle swarm optimization with mutation and reposition,” ICIC International journal, Vol.10, No.6, December 2014.
  26. D. Tian, “Particle Swarm Optimization with Chaotic Maps and Gaussian Mutation for Function Optimization,” International Journal of Grid and Distributed Computing", Vol.8, No.4, 2015, pp.123-134.
  27. R. A. Jamous, A. A. Tharwat, E. EL.Seidy, and B. I. Bayoum, “Modifications of Particle Swarm Optimization Techniques and Its Application on Stock Market: A Survey,” IJACSA Journal, Vol.6, No. 3, 2015, pp.99–108.
  28. J. W. Li, Y. M. Cheng, and K. Z. Chen, “Chaotic particle swarm optimization algorithm based on adaptive inertia weight,” Control and Decision Conference (2014 CCDC), The 26th Chinese. Year: 2014, Pages: 1310 - 1315, DOI: 10.1109/CCDC.2014.6852369, IEEE Conference Publications.
  29. J. J. Craige, “Introduction to Robotics: Mechanics and Control,” Prentice-Hell, Inc., 2005, pp.180.
  30. N. Ramakoti, A. Vinay, and R. K. Jatoth, “Particle Swarm Optimization Aided Kalman Filter for Object Tracking,” IEEE International Conference on Advances in Computing, Control, and Telecommunication Technologies. Date of Conference: 28-29 Dec. 2009, Page(s): 531 – 533, Trivandrum, Kerala, DOI: 10.1109/ACT.2009.135.

Keywords

Circular path, Kalman filter, Particle swarm optimization, Robot manipulator, State space representation, Visual servoing.