TARU PUBLICATIONS
Journal of Information and Optimization Sciences cover

Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

Powered by:Powered by

Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
submissions@tarupublications.com
Taru Online First Current IssueSpecial IssuesBrowse all Issues
Journal of Information and Optimization Sciences
Open Access Research Article

A method to estimate crowd size and crowd count

* , ,

* Corresponding author · click or hover a name for details

pp. 1105–1115Vol. 45Issue 4May 2024DOI: 10.47974/JIOS-1695XML
Published Online:
08 Jun 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1695
Pages:
1105–1115

Abstract

Crowd is known when people in large number gather together. When crowd gather for any reasons, it may cause any serious problem for administration. So authority must know about actual situation of crowd. In recent years need for crowd size estimation, has arisen to manage the crowd well. It can help in managing crowd before happening of huge event, traffic management, providing security to crowd and in many other areas where to manage crowd, some estimation on crowd size is needed. After reviewing existing applications, it can be concluded that there are many approaches for estimating density of a crowd and to count the individual’s, but most of the available approaches did not estimate both crowd density and crowd count together using single model. To overcome these restrictions, a single model is proposed to estimate crowd count and density in the crowded scenes. Here two models are prepared in four stages that work individually as well as forms steps for combined approach. These models firstly pre-process the crowd images extract features and then classify into different classes. Crowd density model classifies images into several density classes. Firstly, dataset of crowd images is prepared according to the requirement using crowd dataset. These crowd images are then sorted and provided to next models. Combined Crowd density and Count Estimation model classifies images into various densities and count classes.

Keywords

CrowdCrowd sizeCrowd countFeature extraction

Subject Classifications

68T1068U10

References

[1] Thanh-Sach Le, Chi-Kien Huynh, Human-crowd density estimation based on gabor filter and cell division, Advanced Computing and Applications (ACOMP), IEEE, 157– 161, (2015). 
[2] S. Abdulla M. Saleh, Shahrel Azmin Suandi, and Haidi Ibrahim, Recent survey on crowd density estimation and counting for visual surveillance, Engineering Applications of Artificial Intelligence, (41):103–114, (2015).
[3] V. Chauhan, S. Kumar, S.Kumar Singh, Human count estimation in high density crowd images and videos, Parallel, Distributed and Grid Computing (PDGC), IEEE, 343–347, (2016). 
[4] Yaocong Hu, Huan Chang, Fudong Nian, Yan Wang, and Teng Li, Dense crowd counting from still images with convolutional neural networks, Journal of Visual Communication and Image Representation, 38:530–539, (2016).
[5] Ven Jyn Kok, Mei Kuan Lim, Chee Seng Chan, Crowd behavior analysis: A review where physics meets biology, Neurocomputing, 177:342–362, (2016).
[6] Muhammad Saqib, Sultan Daud Khan, Michael Blumenstein, Texture-based feature mining for crowd density estimation: A study, Image and Vision Computing New Zealand (IVCNZ), 1–6, (2016).
[7] Yongsang Yoon, Jeonghwan Gwak, Jong-In Song, Moongu Jeon, Conditional marked point process-based crowd counting in sparsely and moderately crowded scenes, Control, Automation and Information Sciences, IEEE 215–220, (2016).
[8] Shiliang Pu,Tao song ,Yuan Zhang,Di Xie, Estimation of crowd density in surveillance scenes based on deep convolutional neural network, Procedia Computer Science 111:154–159, (2017).
[9] Poonia, Ramesh C., and Linesh Raja. “Smart Technologies in Engineering.” Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science) 13.6 : 1172-1172 (2020).
[10] Patel, Hima, et al. “Neural network with deep learning architectures.” Journal of Information and Optimization Sciences 39.1 : 31-38 (2018).
[11] Qi Wang, Junya Gao, Wei Lin,Xuelong Li., NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization, IEEE Transactions on Pattern Analysis and Machine Intelligence,43(6): 2141 – 2149, (2021).
[12] Zhang Q, Chan AB, Wide-area crowd counting: Multi-view fusion networks for counting in large scenes, International Journal of Computer Vision 130(8) : 1938–1960, (2022).
[13] Xiong H, Lu H, Liu C, Liu L, Shen C, Cao Z, from open set to closed set: Supervised spatial divide-and conquer for object counting, International Journal of Computer Vision 131(7) : 1–19, (2023).

Views: 188Downloads: 79Citations: 0