International Journal of Leading Research Publication

E-ISSN: 2582-8010   •   Impact Factor: 9.56

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Monthly Scholarly International Journal

Call for Paper Volume 6 Issue 12 December 2025 Submit your research before last 3 days of to publish your research paper in the issue of December.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 6 Issue 12
December 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

A Review on Real-Time Crowd Monitoring for Emergency Response and Public Safety Using Deep Learning

Author(s) Rupali D. Lodha, Dr.Mahesh R. Sanghavi
Country India
Abstract Crowd monitoring is all about keeping people safe whenever a large number of people gather in one place, like during concerts, sports events, festivals, or in busy public areas like train stations. If big groups of people are not properly managed, it can cause dangerous situations such as stampedes, accidents, or even crimes that go unnoticed in the crowd. Modern crowd monitoring systems act like smart guardians. They keep an eye on the movement of people in real time, using cameras and sensors. These systems don’t just count how many people are there—they also study how people are moving. For example, if people suddenly start running or pushing in one direction, the system can quickly identify that something might be wrong. Apart from watching the crowd as a whole, these systems can also look for specific individuals who may need help. For example, someone might collapse due to a medical emergency, or someone may behave in a suspicious way. The system can send alerts immediately so that security or medical staff can act quickly. To make this monitoring accurate and fast, especially on small and portable devices (like cameras with built-in processors), researchers use advanced computer models. These models are designed to be lightweight (so they don’t slow down devices) and smart. By combining deep learning with attention-based techniques, the system becomes better at spotting unusual activities in a crowd.
Keywords Crowd Monitoring, Public Safety, Real-Time Surveillance, Crowd Behaviour Analysis, Anomaly Detection, Edge Computing.
Field Engineering
Published In Volume 6, Issue 12, December 2025
Published On 2025-12-16
Cite This A Review on Real-Time Crowd Monitoring for Emergency Response and Public Safety Using Deep Learning - Rupali D. Lodha, Dr.Mahesh R. Sanghavi - IJLRP Volume 6, Issue 12, December 2025. DOI 10.70528/IJLRP.v6.i12.1868
DOI https://doi.org/10.70528/IJLRP.v6.i12.1868
Short DOI https://doi.org/hbfr2h
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJLRP DOI prefix is
10.70528/IJLRP

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 6 Isu 10 Cover Page Vol 6 Isu 9 Cover Page Vol 6 Isu 8

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJLRP
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijlrp.com