IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v9y2018i1d10.1007_s13198-016-0431-5.html
   My bibliography  Save this article

Matching images captured from unmanned aerial vehicle

Author

Listed:
  • Steven Lawrence Fernandes

    (Karunya University)

  • G. Josemin Bala

    (Karunya University)

Abstract

Police database cannot have images of first-time offenders; hence, apprehending them becomes a very challenging task. In this paper, we propose a novel technique to apprehend first-time offenders using composite sketches and images captured by unmanned aerial vehicles. The key contribution of this paper is we have developed a new technology to match composite sketches with images captured by unmanned aerial vehicle to apprehend first-time criminals in a very short time period. The unmanned aerial vehicle is sent in the area where the first-time offender is likely to be present. The image captured by unmanned aerial vehicle is passed to face detection module so that only human faces are obtained. Feature extraction is performed using multi-resolution uniform local binary pattern, and classification is performed using dictionary matching. This proposed method is validated by composite sketches generated using SketchCop FACETTE face design system software and images captured by Phantom 3 professional unmanned aerial vehicle.

Suggested Citation

  • Steven Lawrence Fernandes & G. Josemin Bala, 2018. "Matching images captured from unmanned aerial vehicle," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(1), pages 26-32, February.
    • Handle: RePEc:spr:ijsaem:v:9:y:2018:i:1:d:10.1007_s13198-016-0431-5
    DOI: 10.1007/s13198-016-0431-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-016-0431-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-016-0431-5?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sannidhan M. S. & Jason Elroy Martis & Ramesh Sunder Nayak & Sunil Kumar Aithal & Sudeepa K. B., 2023. "Detection of Antibiotic Constituent in Aspergillus flavus Using Quantum Convolutional Neural Network," International Journal of E-Health and Medical Communications (IJEHMC), IGI Global, vol. 14(1), pages 1-26, January.

    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:spr:ijsaem:v:9:y:2018:i:1:d:10.1007_s13198-016-0431-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.