• DocumentCode
    2990425
  • Title

    Real-Time Crowd Massing Risk Supervision System Based on Massing Crowd Counting in Public Venue

  • Author

    Sun Aijun ; Liu Mao ; Li Jianfeng

  • Author_Institution
    Coll. of Environ. Sci. & Eng., Nankai Univ., Tianjin, China
  • fYear
    2009
  • fDate
    18-20 Jan. 2009
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    In this article, based on the FIST model, it proposes four parameters to describe the crowd massing risk in public venues that are the density (D), mutual interaction between each others (I), the personnel characteristics (C) and the impact derived form environmental (E) disturbance on the massing crowd. Then, it carries out the corresponding technical analysis for the four predefined parameters. First, it uses the crowd monitoring systems to estimate the crowd density on the spot; Second, the value of crowd pressure will be obtained through the pressure measurement network which describes the mutual affects between different persons; Third, it has neglected the influence of individual differences on the crowd as a whole; Fourth, it attributes all the influence factors to an index Integrated Disturbance Intensity, then establishes the corresponding mathematical model to quantify its intensity. Finally, it builds up the DICE model as the quantification of crowd massing risk.
  • Keywords
    accident prevention; public administration; risk analysis; FIST model; crowd monitoring systems; integrated disturbance intensity; massing crowd counting; pressure measurement network; public venue; real-time crowd massing risk supervision system; Educational institutions; Image storage; Mathematical model; Monitoring; Motion analysis; Personnel; Pressure measurement; Real time systems; Risk management; Sun;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Network and Multimedia Technology, 2009. CNMT 2009. International Symposium on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-5272-9
  • Type

    conf

  • DOI
    10.1109/CNMT.2009.5374743
  • Filename
    5374743