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Original Article
Real Time Visual Crowd Guidance System
B. Chithra1
T. Ganapathy2
A.J. Anbarasan3
T. Thirumalai4
1 Assistant Professor, Department of Information Technology, PSV College of Engineering and Technology, Krishnagiri,Tamil Nadu, India. 2 3 4 UG Scholars, Department of Information Technology, PSV College of Engineering and Technology, Krishnagiri, Tamil Nadu, India.
Published Online: January-April 2026
Pages: 370-374
Cite this article
No DOIReferences
1. Qudes MB Aljelawy, Entessar K. Hanoun, Ghofran M. Ali, “Real-time People Counting with Deep Learning: A Solution for Crowd
Management,” Journal of University of Mosul, 2025. Explores YOLO-based detection for real-time people counting in dynamic environments.
2. K. Helini, B. Niharika, B. Tejaswini, D. Shriya, K. Anjali, “Real-Time Crowd Counting System Using Machine Learning,” International
Journal of Data Science, 2025. Presents CNN-based crowd counting for live video feeds with high accuracy.
3. Sheela S. Maharajpet, Ananya V. Hegde, “Intelligent Real-Time Crowd Density Estimation for Proactive Event Safety,” IRO Journals, 2025.
Combines YOLOv8 and CSRNet for density estimation and visual analytics dashboards.
4. Z. Chen, X. Xie, T. Qiu et al., “Dense-stream YOLOv8n: Lightweight Framework for Real-Time Crowd Monitoring in Smart Libraries,”
Scientific Reports, 2025. Focuses on real-time monitoring with lightweight deep learning models for embedded systems.
5. N. Jagadeesh Chandra BrammeswaraRao, Adusumilli Varun Kumar, N. Venkatesh Raju,“Crowd Management in Railway System Using
Deep Learning,” International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2025. Focuses on deep
learning-based crowd monitoring and density analysis in railway environments using real-time video processing techniques.
Management,” Journal of University of Mosul, 2025. Explores YOLO-based detection for real-time people counting in dynamic environments.
2. K. Helini, B. Niharika, B. Tejaswini, D. Shriya, K. Anjali, “Real-Time Crowd Counting System Using Machine Learning,” International
Journal of Data Science, 2025. Presents CNN-based crowd counting for live video feeds with high accuracy.
3. Sheela S. Maharajpet, Ananya V. Hegde, “Intelligent Real-Time Crowd Density Estimation for Proactive Event Safety,” IRO Journals, 2025.
Combines YOLOv8 and CSRNet for density estimation and visual analytics dashboards.
4. Z. Chen, X. Xie, T. Qiu et al., “Dense-stream YOLOv8n: Lightweight Framework for Real-Time Crowd Monitoring in Smart Libraries,”
Scientific Reports, 2025. Focuses on real-time monitoring with lightweight deep learning models for embedded systems.
5. N. Jagadeesh Chandra BrammeswaraRao, Adusumilli Varun Kumar, N. Venkatesh Raju,“Crowd Management in Railway System Using
Deep Learning,” International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2025. Focuses on deep
learning-based crowd monitoring and density analysis in railway environments using real-time video processing techniques.
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