Crowd Density Estimation Using Deep Learning: A Convolutional Neural Network Approach for Real-time Monitoring

Jagriti Singh; Khushi Kawade

Title:
Crowd Density Estimation Using Deep Learning: A Convolutional Neural Network Approach for Real-time Monitoring

Authors:
Khushi Kawade | Jagriti Singh

Cite This Article :

Khushi Kawade | Jagriti Singh "Crowd Density Estimation Using Deep Learning: A Convolutional Neural Network Approach for Real-time Monitoring" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-8 | Issue-5, October 2024, pp.472-476, URL: https://www.ijtsrd.com/papers/ijtsrd69396.pdf

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Abstract :
Crowd density estimation is an essential aspect of public safety, urban management, and event monitoring. The emergence of deep learning techniques has revolutionized this domain by providing scalable, efficient, and accurate methods for estimating crowd density in real-time. In this paper, we analyzed the performance of a Convolutional Neural Network (CNN) for crowd density estimation by tracking key metrics like training vs. validation loss, over several epochs. The results demonstrate that the CNN model rapidly converges and generalizes well to unseen data, offering a reliable solution for real-world crowd monitoring applications.

Keywords :
Crowd Density Estimation, Deep Learning, Convolutional Neural Network (CNN), Mean Squared Error (MSE), Mean Absolute Error (MAE), Real-time Monitoring

Publication Details:

Unique Identification Number : IJTSRD69396

Published In : Volume-8 | Issue-5, October 2024

Page Number(s) : 472-476

Publisher Name : IJTSRD | www.ijtsrd.com | E-ISSN 2456-6470

Copyright © 2019 by author(s) and International Journal of Trend in Scientific Research and Development Journal. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)

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