• May 15, 2019 News!Vol.7, No.5- Vol.8, No.4 has been indexed by EI (Inspec).   [Click]
  • Aug 01, 2018 News! [CFP] 2020 the annual meeting of IJMO Editorial Board, ECDMO 2020, will be held in Athens, Greece, February 15-17, 2020.   [Click]
  • Sep 30, 2019 News!Vol 9, No 6 has been published with online version. 12 original aritcles from 6 countries are published in this issue.    [Click]
General Information
    • ISSN: 2010-3697  (Online)
    • Abbreviated Title: Int. J. Model. Optim.
    • Frequency: Bimonthly
    • DOI: 10.7763/IJMO
    • Editor-in-Chief: Prof. Adrian Olaru
    • Executive Editor: Ms.Yoyo Y. Zhou
    • Abstracting/ Indexing: ProQuest, Crossref, Electronic Journals Library, Google Scholar, EI (INSPEC, IET), EBSCO, etc.
    • E-mail ijmo@iacsitp.com
Prof. Adrian Olaru
University Politehnica of Bucharest, Romania
I'm happy to take on the position of editor in chief of IJMO. It's a journal that shows promise of becoming a recognized journal in the area of modelling and optimization. I'll work together with the editors to help it progress.
IJMO 2012 Vol.2(3): 227-229 ISSN: 2010-3697
DOI: 10.7763/IJMO.2012.V2.116

Visual Object Tracking Using Fuzzy-based Thresholding and Kalman Filter

Hamidreza Rashidy Kanan and Parasto Karimi

Abstract—Analysis of movement and detection of objects are important branches of image processing and computer vision due to their promising applications such as robotics, industrial automation and the military. In this paper, we propose a new approach for visual object tracking using fuzzy-based thresholding and Kalman filter. In the proposed algorithm, knowledge of three different thresholding methods (i.e. mode method, iterative thresholding and double thresholding) is used to create “if-then” fuzzy rules. The designed fuzzy-based thresholding method combines the mentioned three different thresholds in order to provide the appropriate threshold which will be utilized to segment the object from the background. Finally, the segmented frame is applied to a Kalman filter to predict the next path when the object moves. To evaluate the effectiveness of the proposed method, we compared the obtained position of the object based on the proposed method with the results of the gravity center method and also their real positions. The experimental results show that the proposed approach can improve the tracking stabilization when objects go across complex backgrounds.

Index Terms—Visual object tracking, segmentation, fuzzy, kalman filter.

Authors are with the Electrical, Computer and IT Engineering Department, Qazvin Branch, Islamic Azad University, Qazvin, IRAN. (Tel.: (+98) 281 3665275; fax: (+98) 281 3665279; zip code: 34185-1416. e-mail: h.rashidykanan@qiau.ac.ir)


Cite: Hamidreza Rashidy Kanan and Parasto Karimi, "Visual Object Tracking Using Fuzzy-based Thresholding and Kalman Filter," International Journal of Modeling and Optimization vol. 2, no. 3, pp. 227-229, 2012.

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