IDEAS home Printed from https://ideas.repec.org/a/zib/zbnaim/v4y2020i1p22-25.html
   My bibliography  Save this article

Iot Based Statistical Approach For Human Crowd Density Estimation-Design And Analysis

Author

Listed:
  • Jugal Kishor Gupta

    (Vidya College of Engineering Meerut UP)

  • Sanjay Kumar Gupta

    (BIET Jhanshi UP)

Abstract

In this paper we present an IoT based solution that can reduce the complexity of crowd estimation. About the human crowd estimation many technique are in existence but now a day’s more work are going on in the field of IoT, because this is era of IoT and most of the every organization is shifted towards IoT based system. So we are also proposed this system in this field and we are using the Respberry Pi-3 which are having quad core processor that can very useful and gives better result and gives accurate number even in the humans are very close to each others. This IoT based model can easily implements in the crowded areas and monitor the same in this area. The camera module in this model also helps to differentiate between human and other bodies. As this is a mobile model it can easily fix on the walls of street light and in the time of dark or in night the camera capture clear image for process in the presence of street light. So that this model gives better result almost 70% better result in compare to exiting approaches.

Suggested Citation

  • Jugal Kishor Gupta & Sanjay Kumar Gupta, 2020. "Iot Based Statistical Approach For Human Crowd Density Estimation-Design And Analysis," Acta Informatica Malaysia (AIM), Zibeline International Publishing, vol. 4(1), pages 22-25, June.
    • Handle: RePEc:zib:zbnaim:v:4:y:2020:i:1:p:22-25
    DOI: 10.26480/aim.01.2020.22.25
    as

    Download full text from publisher

    File URL: https://actainformaticamalaysia.com/download/1138/
    Download Restriction: no
    File URL: https://libkey.io/10.26480/aim.01.2020.22.25?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
    ---><---

    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:zib:zbnaim:v:4:y:2020:i:1:p:22-25. 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: Zibeline International Publishing (email available below). General contact details of provider: https://actainformaticamalaysia.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.