IDEAS home Printed from https://ideas.repec.org/a/igg/jdst00/v12y2021i1p16-29.html
   My bibliography  Save this article

Reliability Analysis of Multi-Objective Spatio-Temporal Segmentation of Human Motion in Video Sequences

Author

Listed:
  • Yan Hu

    (Capital Normal University, China)

Abstract

In view of the problem of uneven distribution of edge contour of multi-target human motion image in video sequence, which leads to the decline of target detection ability, an algorithm of multi-target spatial-temporal segmentation of human motion in video sequence based on edge contour feature detection and block fusion is proposed. Firstly, a multi-target spatial-temporal detection model of human motion in video sequence was constructed, extracting video image frame sequence, using discrete frame fusion method to segment and fuse moving target image, matching moving multi-target in video sequence, secondly segmenting motion features in moving target image, combining with SURF algorithm (speeded up robust features, accelerated robust features) to detect and extract human motion objects in video sequence. The experimental results show that the gray histogram of human motion multi-target space-time segmentation is close to the original image histogram, and the detection and recognition ability of human motion target is improved.

Suggested Citation

  • Yan Hu, 2021. "Reliability Analysis of Multi-Objective Spatio-Temporal Segmentation of Human Motion in Video Sequences," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 12(1), pages 16-29, January.
    • Handle: RePEc:igg:jdst00:v:12:y:2021:i:1:p:16-29
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDST.2021010102
    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:jdst00:v:12:y:2021:i:1:p:16-29. 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.