Object Speed Estimation by using Fuzzy Set
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Object Speed Estimation by using Fuzzy Set

Authors: Hossein Pazhoumand-Dar, Amir Mohsen Toliyat Abolhassani, Ehsan Saeedi

Abstract:

Speed estimation is one of the important and practical tasks in machine vision, Robotic and Mechatronic. the availability of high quality and inexpensive video cameras, and the increasing need for automated video analysis has generated a great deal of interest in machine vision algorithms. Numerous approaches for speed estimation have been proposed. So classification and survey of the proposed methods can be very useful. The goal of this paper is first to review and verify these methods. Then we will propose a novel algorithm to estimate the speed of moving object by using fuzzy concept. There is a direct relation between motion blur parameters and object speed. In our new approach we will use Radon transform to find direction of blurred image, and Fuzzy sets to estimate motion blur length. The most benefit of this algorithm is its robustness and precision in noisy images. Our method was tested on many images with different range of SNR and is satisfiable.

Keywords: Blur Analysis, Fuzzy sets, Speed estimation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1060331

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References:


[1] D. J. Dailey and L. Li, "An algorithm to estimate vehicle speed using un-calibrated cameras intelligent transportation system," Proceedings 1999 IEEE/IEEJ/JSAI International Conference, 1999, pp.441-446.
[2] T.N. Schoepflin and D.J. Dailey "Algorithms for Calibrating Roadside Traffic Cameras and Estimating Mean Vehicle Speed,", Proceedings of the IEEE International Symposium on Intelligent Vehicles 2004, Parma, Italy, 2004, pp. 277-283.
[3] M. Yu, G. Jiang, and B. Yu, "An integrative method for video based traffic parameter extraction in ITS," IEEE APCCAS, 2000, pp.136-. 139.
[4] M.E. Moghaddam, and M. Jamzad, "Patterns Finding point spread function of motion blur using Radon transform and modeling the motion length", Proceedings of the Fourth IEEE International Symposium. Bologna, Italy, 2004, pp. 314- 317.
[5] Peter Toft, "The Radon Transform - Theory and. Implementation", Ph.D. thesis, Department of Mathematical. Modelling, Technical Univ. of Denmark, June 1996 .
[6] M.E. Moghaddam, and M. Jamzad, "Blur identification in noisy images using radon transform and power spectrum modeling." Proceedings of 12th international workshop on systems, signals & image processing. Chalkida, Greece, 2005, pp. 347-352.
[7] M. R. Banham and A. K. Katsaggelos, "Digital image restoration," IEEE Signal Processing Magazine, vol. 14, no. 2, pp. 24-41, 1997
[8] LI Q, YOSHIDA Y. "Parameter estimation and restoration for motion blurred images ", IEICE Transaction Fundamentals, pp 1430-1437,1997
[9] H. J. Zimmermann, Fuzzy Set Theory, Kluwer Academic, Dor- drecht, The Netherlands, 1996
[10] H.Y.Lin, Object Speed Measurements Using Motion Blurred Images, CCU VisionLab, (Online). Available :www.cse.nsysu.edu.tw /seminar /93/050603.ppther, Location, Date..