IDEAS home Printed from https://ideas.repec.org/a/igg/jcini0/v14y2020i1p63-74.html
   My bibliography  Save this article

Moving Target Detection and Tracking Based on Improved FCM Algorithm

Author

Listed:
  • Wang Ke Feng

    (Jiangsu University of Technology, Changzhou, China)

  • Sheng Xiao Chun

    (Jiangsu University of Technology, Changzhou, China)

Abstract

With the rapid development of computer intelligence technology, the majority of scholars have a great interest in the detection and tracking of moving targets in the field of video surveillance and have been involved in its research. Moving target detection and tracking has also been widely used in military, industrial control, and intelligent transportation. With the rapid progress of the social economy, the supervision of traffic has become more and more complicated. How to detect the vehicles on the road in real time, monitor the illegal vehicles, and control the illegal vehicles effectively has become a hot issue. In view of the complex situation of moving vehicles in various traffic videos, the authors propose an improved algorithm for effective detection and tracking of moving vehicles, namely improved FCM algorithm. It combines traditional FCM algorithm with genetic algorithm and Kalman filter algorithm to track and detect moving targets. Experiments show that this improved clustering algorithm has certain advantages over other clustering algorithms.

Suggested Citation

  • Wang Ke Feng & Sheng Xiao Chun, 2020. "Moving Target Detection and Tracking Based on Improved FCM Algorithm," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 14(1), pages 63-74, January.
  • Handle: RePEc:igg:jcini0:v:14:y:2020:i:1:p:63-74
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCINI.2020010105
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jcini0:v:14:y:2020:i:1:p:63-74. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.