Crowd Density Prediction using Deep Learning

May 2025
Vol-11, Issue-3
Paper ID: 26694
ISSN: 2395-4396
Downloads: 0

Abstract & Details

Research Area
Computer Science and Engineering
Keywords
Human Detection Crowd Monitoring Deep Learning YOLOv5 Real-Time Analytics Public Safety Computer Vision Surveillance Telegram Alerts Density Estimation.
Abstract
As urban areas grow and public events become more common, the need for efficient crowd management is increasingly critical—especially at high-traffic locations such as tourist destinations and religious sites. Traditional methods like manual headcounts or static surveillance systems often lack the precision and scalability needed for modern crowd control. To address these limitations, this project introduces a real-time human density monitoring system powered by the YOLOv5 deep learning model. By analyzing video feeds, the system can detect and count individuals, issuing instant alerts via Telegram when crowd density exceeds safe levels. This allows authorities to take prompt action and prevent overcrowding. The system is implemented using Python and integrates a pre-trained YOLOv5 model within a real-time processing pipeline. Surveillance footage is captured and processed frame-by-frame to detect humans using bounding boxes. The number of detected individuals is used to compute crowd density, and alerts are triggered if it crosses the predefined safety limit. Built in the PyCharm environment, the solution minimizes human involvement while ensuring real-time functionality and high scalability. The alert mechanism utilizes the python-telegram-bot library for seamless communication with relevant personnel.
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

# Name Institute / Affiliation
1 Abdul Jabbar Shaikh Vidya Vikas Institute of Engineering & Technology, Karnataka, India
2 Ankush K Vidya Vikas Institute of Engineering & Technology, Karnataka, India
3 Fathima Zehra Vidya Vikas Institute of Engineering & Technology, Karnataka, India
4 Tasmiya Vidya Vikas Institute of Engineering & Technology, Karnataka, India

How to Cite

Use the following formats to cite this article in your research.

APA Style
Shaikh, Abdul Jabbar, K, Ankush, Zehra, Fathima, & Tasmiya (2025). Crowd Density Prediction using Deep Learning. International Journal of Advance Research and Innovative Ideas In Education, 11(3), 2107-2113.
MLA Style
Shaikh, Abdul Jabbar, et al. "Crowd Density Prediction using Deep Learning." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 3, 2025, pp. 2107-2113.
IEEE Style
Abdul Jabbar Shaikh, Ankush K, Fathima Zehra, and Tasmiya, "Crowd Density Prediction using Deep Learning," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 3, pp. 2107-2113, 2025.
Vancouver Style
Shaikh Abdul Jabbar, K Ankush, Zehra Fathima, Tasmiya. Crowd Density Prediction using Deep Learning. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(3):2107-2113.
Harvard Style
Shaikh, Abdul Jabbar, K, Ankush, Zehra, Fathima, & Tasmiya (2025) 'Crowd Density Prediction using Deep Learning', International Journal of Advance Research and Innovative Ideas In Education, 11(3), pp. 2107-2113.
Chicago Style
Shaikh, Abdul Jabbar, et al. "Crowd Density Prediction using Deep Learning." International Journal of Advance Research and Innovative Ideas In Education 11, no. 3 (2025): 2107-2113.
Turabian Style
Shaikh, Abdul Jabbar, et al. "Crowd Density Prediction using Deep Learning." International Journal of Advance Research and Innovative Ideas In Education 11, no. 3 (2025): 2107-2113.

Export Citation

Related Research

Comprehensive Review of Existing Chatbot Systems for Career Assistance, Resume Support, and ATS-Aware Guidance
Amruta Bargal et al. 2026 Data science
PDF Unavailable
Development of an AI-Powered Multimodal Web Assistant with Intelligent Resume Building and ATS Enhancement
Amruta Bargal et al. 2026 Data science
PDF Unavailable
A Deep Learning-Based Framework for Mood-Oriented Music Recommendation Using Facial Expression Analysis
Vaibhav Ashok Bhangare et al. 2026 Computer Engineering
PDF Unavailable
Survey On : Intelligent Payroll and Human Resource Management Systems: A Systematic Review of Automation, Security, and Analytics
Vishakha Jadhav et al. 2026 Human Resource Management, Artificial Intelligence, Machine Learning, Payroll Systems, Cybersecurity, Business Intelligence, Robotic Process Automation, Employee Analytics, Digital Transforma
PDF Unavailable
Civic Engagement & Empowerment Platform
Supriya Dadaso Bankar et al. 2026 Computer engineering
PDF Unavailable
Recent Developments in Microneedle Technology and Its Diverse Biomedical Applications
Diksha Patil 2026 Technology
PDF Unavailable
RAG System Development with Pydantic AI ChromaDB & Groq
Prof. Priyanka P. Kakade et al. 2026 Computer Engineering
PDF Unavailable
Machine Learning Based Early Stage Diabetes Detection System
Rohan Mulik et al. 2026 Computer Engineering
PDF Unavailable
A Survey on Skillsense:AI Career Analyzer App
Kirti Datir et al. 2026 Computer Engineering
PDF Unavailable
Employee Performance Portal
P.Harika et al. 2026 Computer science and engineering
PDF Unavailable