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

Khushi, Kawade and Jagriti, Singh (2024) Crowd Density Estimation Using Deep Learning: A Convolutional Neural Network Approach for Real-time Monitoring. International Journal of Trend in Scientific Research and Development, 8 (5). pp. 472-476. ISSN 2456-6470

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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.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	06 Nov 2024 12:15
Last Modified:	06 Nov 2024 12:15
URI:	http://eprints.umsida.ac.id/id/eprint/14565

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