research-article

Automated Solutions for Crowd Size Estimation

Authors:

Muhammad Waqar Aziz,

Muhammad Hamad Alizai,

Khan Bahadar KhanAuthors Info & Claims

Social Science Computer Review, Volume 36, Issue 5

Pages 610 - 631

https://doi.org/10.1177/0894439317726510

Published: 01 October 2018 Publication History

Abstract

The crowd phenomenon frequently occurs in dense urban living environments. Crowd counting or estimation helps to develop management strategies such as designing safe public places and evacuation plan for emergencies. These strategies are different depending upon the type of event such as political and public demonstrations, sports, and religious events. However, estimating the number of people in crowds at closed or open environments is quite challenging because of the dynamics involved in the process. In addition, crowd estimation itself poses challenges due to randomness in crowd behavior, motion, and an area's geometric specifications. Crowd behavior as well as the area parameters is studied before suggesting any possible technological solution for managing a crowd. This article presents a theoretical understanding of the major crowd size estimation approaches that cannot be achieved through the study of existing survey papers in this area, because the existing survey papers focus on particular technologies/specific areas with no or brief description of the involved steps. Besides, this article also highlights the strength and weakness of crowd size estimation solutions and their possible applications. It is, therefore, believed that the provided information would assist in developing an intelligent system for crowd management.

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Cited By

Determe JSingh UHorlin FDe Doncker P(2021)Forecasting Crowd Counts With Wi-Fi Systems: Univariate, Non-Seasonal ModelsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.299210122:10(6407-6419)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1109/TITS.2020.2992101

Automated Solutions for Crowd Size Estimation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

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Published: 01 October 2018

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Cited By

Determe JSingh UHorlin FDe Doncker P(2021)Forecasting Crowd Counts With Wi-Fi Systems: Univariate, Non-Seasonal ModelsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.299210122:10(6407-6419)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1109/TITS.2020.2992101

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