Recent Research Reviews Journal
IRO Journals
Volume 4 — Issue 2 — December 2025

Intelligent Real-Time Crowd Density Estimation for Proactive Event Safety: A Machine Learning Approach

https://doi.org/10.36548/rrrj.2025.2.005
Sheela S Maharajpet
Department of MCA, Acharya Institute of Technology, Bangalore, India
Ananya V Hegde
Department of MCA, Acharya Institute of Technology, Bangalore, India
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Maharajpet, Sheela S, and Ananya V Hegde. 2025. “Intelligent Real-Time Crowd Density Estimation for Proactive Event Safety: A Machine Learning Approach”. Recent Research Reviews Journal 4 (2): 264-80. https://doi.org/10.36548/rrrj.2025.2.005.
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Keywords

— Crowd Density Estimation
— Real-Time Monitoring
— Machine Learning
— Deep Learning
— YOLOv8
— CSRNet
— Event Safety
— Crowd Management
Published: 26-09-2025

Abstract

High-density crowd events like public concerts, sporting events, or religious festivals represent significant safety challenges due to high crowd density. Methods of monitoring crowds using manual observation or passive surveillance often don't provide real-time information. Therefore, we present a real-time crowd density estimation solution that utilizes YOLOv8 for people detection and CSRNet for density estimation. The crowd density estimation system utilizes live video feeds from direct surveillance cameras/CCTV or drone footage. The system will assess the crowd density in four levels across four distinct areas: Low, Medium, High, or Critical crowd density. The density estimator has a web-based dashboard that provides real-time analytics reports, heatmap density estimates, and historical records, which can assist in making quick, informed decisions and assessments following an event. The system is validated on benchmark datasets and real-world video streams with 95.3% detection accuracy, 7.4 MAE in crowd counting and 28 FPS processing with off-the-shelf GPU hardware. The results show high accuracy with low latency, making it feasible for real-world applications for large-scale events. The main contributions of the work include using YOLOv8 integrated with CSRNet to jointly detect and estimate crowds, developing a real-time dashboard to provide transparent crowd analytics, and system validation with quantitative metrics and real-world evidence.

References

  1. Kamra, Vikas, Aditya Vaishnav, Anilansh Verma, Rehan Khan, and Shubham Singh. "A Novel Approach for Crowd Analysis and Density Estimation by Using Machine Learning Techniques." In 2024 International Conference on Intelligent Systems for Cybersecurity (ISCS), IEEE, (2024): 1-6.
  2. Taha, Mohammad, Farah Bata, Odai Dweik, and Rahma Atallah. "A Real-Time Crowd Estimation Based on RSSI Measurements Using Convolutional Neural Network." In 2023 IEEE 11th Conference on Systems, Process & Control (ICSPC), IEEE, (2023): 159-164.
  3. Mallick, Sarbartha Sankar, Indrajit Das, Sreya Das, and Raja Karmakar. "An Intelligent Crowd Density and Motion Direction Estimation for Real-Time Crowd Dynamics." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE, (2024): 1-6.
  4. Kim, GyeongYeon, EunSeo Ko, DongRyeong Kim, JuEun Kim, Dane Hindsley, and Eric T. Matson. "Real-Time Crowd Density Estimation and Stampede Risk Assessment System Using Thermal Camera." In 2024 Eighth IEEE International Conference on Robotic Computing (IRC), IEEE, (2024): 178-185.
  5. Rong, Liangzi, and Chunping Li. "Coarse-and fine-grained attention network with background-aware loss for crowd density map estimation." In Proceedings of the IEEE/CVF winter conference on applications of computer vision, (2021): 3675-3684.
  6. Zhang, Jing, Wen Zhang, and Ximei Wang. "Crowd Density Estimation and Motion Trajectory Detection in Video Surveillance." In 2023 IEEE International Conference on Smart Internet of Things (SmartIoT), IEEE, (2023): 233-237.
  7. Shah, Malay, Sayal Goyal, and Sanket Salvi. "Crowd Density Estimation and Real-time Monitoring using Digital Twins in Transportation Hubs." In 2024 IEEE Pune Section International Conference (PuneCon), IEEE, (2024): 1-6.
  8. Zhang, Zehui, and Xuehong Sun. "Crowd Density Estimation Based on Convolutional Neural Network." In 2020 IEEE 20th International Conference on Communication Technology (ICCT), IEEE, (2020): 1509-1513.
  9. Rong, Liangzi, and Chunping Li. "Coarse-and fine-grained attention network with background-aware loss for crowd density map estimation." In Proceedings of the IEEE/CVF winter conference on applications of computer vision, (2021): 3675-3684.
  10. Subramaniam, Abhishek, Vatsal G. Shah, Preet D. Tibrewala, and Rupali Kale. "Crowd Count Estimator Application using CSRNet." In 2021 International Conference on Intelligent Technologies (CONIT), IEEE, (2021): 1-6.
  11. Zhang, Peng, Weimin Lei, Xinlei Zhao, Lijia Dong, and Zhaonan Lin. "NF-Net: Near and Far Network for Crowd Counting." In 2023 8th International Conference on Computer and Communication Systems (ICCCS), IEEE, (2023): 475-481.
  12. Determe, Jean-François, Sophia Azzagnuni, Utkarsh Singh, François Horlin, and Philippe De Doncker. "Monitoring large crowds with WiFi: A privacy-preserving approach." IEEE Systems Journal 16, no. 2 (2022): 2148-2159.
  13. Jiang, Shengqin, Xiaobo Lu, Yinjie Lei, and Lingqiao Liu. "Mask-aware networks for crowd counting." IEEE Transactions on Circuits and Systems for Video Technology 30, no. 9 (2019): 3119-3129.
  14. Jiang, Min, Chen Zhou, and Jun Kong. "AOH: Online multiple object tracking with adaptive occlusion handling." IEEE Signal Processing Letters 29 (2022): 1644-1648.
  15. Alashban, Adal, Alhanouf Alsadan, Norah F. Alhussainan, and Ridha Ouni. "Single convolutional neural network with three layers model for crowd density estimation." IEEE Access 10 (2022): 63823-63833.
  16. Khan, Muhammad Asif, Hamid Menouar, and Ridha Hamila. "Crowd density estimation using imperfect labels." In 2023 IEEE International Conference on Consumer Electronics (ICCE), IEEE, (2023): 1-6.
  17. Denis, Stijn, Rafael Berkvens, Ben Bellekens, and Maarten Weyn. "Large scale crowd density estimation using a sub-GHz wireless sensor network." In 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), IEEE, (2018): 849-855.
  18. Yanqin, Wan, Yu Zujun, Wang Yao, and Li Xingxin. "Crowd density estimation based on conditional random field and convolutional neural networks." In 2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), IEEE, (2019): 1814-1819.
  19. Maharajpet, Sheela S, Deepa Mugad, and Nagaraj C. “Advanced Safety Helmet Detection: Enhancing Industrial Site Safety with AI.” In Convergence of Machine Learning and IoT for Enabling the Future of Intelligent Systems, edited by Ratnakirti Roy, Manish Kumar Thakur, and Anushree Raj. QTanalytics India, 2024. https://doi.org/10.48001/978-81-966500-7-0-1.
  20. Tahira, Nusrat J., Patel D. Suresh, and Jang S. Park. "Deep Learning based Approach for Crowd Density Estimation and Flow Prediction." In 2024 24th International Conference on Control, Automation and Systems (ICCAS), IEEE, (2024): 1274-1275.
  21. Wei, Xinlei, Junping Du, Meiyu Liang, and Zhe Xue. "Crowd density field estimation based on crowd dynamics theory and social force model." Chinese Journal of Electronics 28, no. 3 (2019): 521-528.
  22. Hongquan, Song, Liu Xuejun, Lü Guonian, Zhang Xingguo, and Wang Feng. "Video scene invariant crowd density estimation using geographic information systems." China Communications 11, no. 11 (2014): 80-89.
  23. Menevse, M. Mert, Melisa Gozet, Asim Egemen Yilmaz, and Mehmet Karakose. "An Analysis Approach for Crowds with Varying Density and Complexity Using Image Data." In 2025 29th International Conference on Information Technology (IT), IEEE, (2025): 1-4.
  24. Wang, Hui, WenQiu Hu, ZhiQiang Wang, ZiHang Gao, and Chao Sun. "Research on Adaptive Density Normalized Crowd Counting Algorithm." In 2023 IEEE International Conference on Mechatronics and Automation (ICMA), IEEE, (2023): 31-35.
  25. Castellano, Giovanna, Corrado Mencar, Gaetano Sette, Francesco Saverio Troccoli, and Gennaro Vessio. "Crowd flow detection from drones with fully convolutional networks and clustering." In 2022 International Joint Conference on Neural Networks (IJCNN), IEEE, (2022): 1-8