Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)

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Crowd Counting in Religious Places Using YOLOv11 With Illumination-Based Image Augmentation

Authors
R. Smaran1, *, V. Kalyan Ram1, S. Sakthi Mahendran1, M. Kiruthiga Devi2
1Student, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani, Chennai, 600026, Tamil Nadu, India
2Assistant Professor, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani, Chennai, 600026, Tamil Nadu, India
*Corresponding author. Email: sr7431@srmist.edu.in
Corresponding Author
R. Smaran
Available Online 31 October 2025.
DOI
10.2991/978-94-6463-866-0_37How to use a DOI?
Keywords
Crowd Counting; Surveillance; Light Variations; Feature Extraction; YOLOv11; Fine-Tuning; Illumination-based Image Augmentation; Adaptive Learning
Abstract

Religious places attract huge crowds and the density of massive crowds which are measured by manual crowd counting and monitoring by surveillance are not effective. Light variations in religious place premises present a great challenge to modern machine learning (ML) techniques as well as traditional-based sensor approaches implemented for crowd counting. These result in poor accuracy in identifying crowd-density estimation tasks. Traditional crowd counting models struggle to maintain consistency when transitioning from light areas to dark scenes, resulting in miscounts and failure to extract features. This work uses YOLOv11 for crowd counting, wherein fine-tuning is performed on an Illumination-based Image-augmented dataset for better adaptability to lighting changes. By including different illumination-based augmentation techniques like brightness, contrast, gray-scale, and glare during training, the model is able to generalize well across different temple settings. The model extracts key features and learns from different conditions, allowing it to adapt well to various temple settings.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
31 October 2025
ISBN
978-94-6463-866-0
ISSN
2589-4919
DOI
10.2991/978-94-6463-866-0_37How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - R. Smaran
AU  - V. Kalyan Ram
AU  - S. Sakthi Mahendran
AU  - M. Kiruthiga Devi
PY  - 2025
DA  - 2025/10/31
TI  - Crowd Counting in Religious Places Using YOLOv11 With Illumination-Based Image Augmentation
BT  - Proceedings of the International Conference on Intelligent Systems and Digital Transformation (ICISD 2025)
PB  - Atlantis Press
SP  - 437
EP  - 449
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-866-0_37
DO  - 10.2991/978-94-6463-866-0_37
ID  - Smaran2025
ER  -