IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/7453744.html
   My bibliography  Save this article

An Effective Video Summarization Framework Based on the Object of Interest Using Deep Learning

Author

Listed:
  • Hafiz Burhan Ul Haq
  • Muhammad Asif
  • Maaz Bin Ahmad
  • Rehan Ashraf
  • Toqeer Mahmood
  • Nadeem Qazi

Abstract

The advancements in digital video technology have empowered video surveillance to play a vital role in ensuring security and safety. Public and private enterprises use surveillance systems to monitor and analyze daily activities. Consequently, a massive volume of data is generated in videos that require further processing to achieve security protocol. Analyzing video content is tedious and a time-consuming task. Moreover, it also requires high-speed computing hardware. The video summarization concept has emerged to overcome these limitations. This paper presents a customized video summarization framework based on deep learning. The proposed framework enables a user to summarize the videos according to the Object of Interest (OoI), for example, person, airplane, mobile phone, bike, and car. Various experiments are conducted to evaluate the performance of the proposed framework on the video summarization (VSUMM) dataset, title-based video summarization (TVSum) dataset, and own dataset. The accuracy of VSUMM, TVSum, and own dataset is 99.6%, 99.9%, and 99.2%, respectively. A desktop application is also developed to help the user summarize the video based on the OoI.

Suggested Citation

  • Hafiz Burhan Ul Haq & Muhammad Asif & Maaz Bin Ahmad & Rehan Ashraf & Toqeer Mahmood & Nadeem Qazi, 2022. "An Effective Video Summarization Framework Based on the Object of Interest Using Deep Learning," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-25, May.
    • Handle: RePEc:hin:jnlmpe:7453744
    DOI: 10.1155/2022/7453744
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/mpe/2022/7453744.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/mpe/2022/7453744.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2022/7453744?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:7453744. 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.