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

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

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

	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 = {},
	doi = {10.5120/ijca2016910685},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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.


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