IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v2y2017i6idhcseit1726323.html

Human Action Recognition Using SURF and HOG Features from Video Sequences

Author

Listed:
  • Akila M
  • Rajeswari R

Abstract

Human action recognition is important in a number of applications such as video indexing, video surveillance and human computer interaction. Hence, human action recognition has been greatly researched during the last decades; however, it is still regarded as a challenging task. In this paper, a human action recognition method is proposed which aims to improve action recognition using a combination of local and global features. For the local feature Speeded-Up Robust Features (SURF) are used and for global feature Histogram of Oriented Gradients (HOG) are used. Bag-of-Features representation of local features is used for representation of the features extraction and the actions are classified using Support Vector Machine. The proposed human action recognition system is tested with various action categories of videos from the KTH dataset. The experimental results show that the proposed method has better results in terms of accuracy.

Suggested Citation

  • Akila M & Rajeswari R, 2017. "Human Action Recognition Using SURF and HOG Features from Video Sequences," 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(6), pages 1259-1264, December.
    • Handle: RePEc:jbh:ijsrcs:v2:y2017:i6:id:hcseit1726323
    Note: Article URL: https://ijsrcseit.com/CSEIT1726323
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/CSEIT1726323
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/paper/CSEIT1726323.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:i6:id:hcseit1726323. 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.