IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/1574054.html
   My bibliography  Save this article

Object Tracking with Multi-Classifier Fusion Based on Compressive Sensing and Multiple Instance Learning

Author

Listed:
  • Si Chen
  • Xiaoshun Lu
  • Xiaosen Chen
  • Min Chen
  • Jianghu Chen
  • Dahan Wang
  • Shunzhi Zhu

Abstract

Object tracking is a critical research in computer vision and has attracted significant attention over the past few years. However, the traditional object tracking algorithms often suffer from the object drifting problem due to various challenging factors in complex environments such as object occlusion and background clutter. This paper proposes a robust and effective object tracking algorithm, called MCM, which combines compressive sensing and online multiple instance learning in a multi-classifier fusion framework. In this framework, we integrate the different discriminative classifiers by learning the varied and compressed feature vectors based on different random projection matrices. And then an improved online multiple instance learning mechanism SMILE is adopted, which introduces the relative similarity to select and weight the instances in the positive bag. The experiments show that the proposed algorithm can improve the performance of object tracking on the challenging video sequences.

Suggested Citation

  • Si Chen & Xiaoshun Lu & Xiaosen Chen & Min Chen & Jianghu Chen & Dahan Wang & Shunzhi Zhu, 2020. "Object Tracking with Multi-Classifier Fusion Based on Compressive Sensing and Multiple Instance Learning," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-17, March.
  • Handle: RePEc:hin:jnlmpe:1574054
    DOI: 10.1155/2020/1574054
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2020/1574054.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/MPE/2020/1574054.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2020/1574054?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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:hin:jnlmpe:1574054. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.