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DOI: 10.14569/IJACSA.2020.0110123
PDF

Open Challenges for Crowd Density Estimation

Author 1: Shaya A Alshaya

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 1, 2020.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Nowadays, many emergency systems and surveillance systems are related to the management of the crowd. The supervision of a crowded area presents a great challenge especially when the size of the crowd is unknown. This issue presents a point of start to the field of the estimation of the crowd based on density or counts. The density of a crowded area is one of the important topics dealt with in many kinds of applications like surveillance, security, biology, traffic. In this paper, we try not only to present a deep review of the different approaches/techniques used in the previous works to estimate the size of the crowd but also to describe the different datasets used. A comparison of some related works based on the weakness and the strength features of each approach is highlighted to show the important research key related to the field of the estimation of the crowded area.

Keywords: Crowd density; count density; deep learning; CNN; datasets; metrics

Shaya A Alshaya, “Open Challenges for Crowd Density Estimation” International Journal of Advanced Computer Science and Applications(IJACSA), 11(1), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110123

@article{Alshaya2020,
title = {Open Challenges for Crowd Density Estimation},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110123},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110123},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {1},
author = {Shaya A Alshaya}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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