IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v2y2017i2idhcseit1722107.html

Optimizing and Background Learning in a Single Process of Moving Object Detection

Author

Listed:
  • R. Usha
  • S. Yamuna
  • R. Deepa

Abstract

Video surveillance systems have long been in use to monitor security sensitive areas. The making of video surveillance systems "smart" requires fast, reliable and robust algorithms for moving object detection, classification, tracking and activity analysis. Moving object detection is the basic step for further analysis of video. It handles segmentation of moving objects from stationary background objects. Object classification step categorizes detected objects into preened classes such as human, vehicle, animal, clutter, etc. It is necessary to distinguish objects from each other in order to track and analyse their actions reliably. In previous system performed background subtraction by using Canny Edge Detection. In Canny Edge Detection process we are taking two images for comparison those are background image and foreground image.

Suggested Citation

  • R. Usha & S. Yamuna & R. Deepa, 2017. "Optimizing and Background Learning in a Single Process of Moving Object Detection," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 2(2), pages 389-393, April.
    • Handle: RePEc:jbh:ijsrcs:v2:y2017:i2:id:hcseit1722107
    Note: Article URL: https://ijsrcseit.com/CSEIT1722107
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/CSEIT1722107
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/paper/CSEIT1722107.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;

    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:jbh:ijsrcs:v2:y2017:i2:id:hcseit1722107. 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: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    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.