Real Time Crowd Counting System Using Machine Learning

K., Helini and B., Niharika and B., Tejaswini and D., Shriya and K., Anjali (2025) Real Time Crowd Counting System Using Machine Learning. Journal of Data Science, 2025 (03). pp. 1-10. ISSN 2805-5160

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Abstract

Crowd counting is a critical task in public safety, event management, and urban planning. This paper presents a real-time crowd counting system leveraging machine learning to accurately estimate the number of people in a given scene. The proposed system employs a convolutional neural network (CNN)-based deep learning model, optimized for processing images and video streams to identify and count individuals in diverse environments. Key features of the system include real-time inference, robust performance in varying lighting and density conditions, and adaptability to different camera perspectives. The model is trained on a diverse dataset, encompassing crowded events, open spaces, and public gatherings, ensuring its versatility and reliability. Post-training, the system is deployed using lightweight architectures, allowing seamless integration with edge devices and IoT platforms.

Item Type:	Article
Uncontrolled Keywords:	Convolutional Neural Networks (CNN), Deep Learning; Image Processing, Video Analytics, YOLO
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	19 Jun 2025 09:18
Last Modified:	19 Jun 2025 09:18
URI:	http://eprints.intimal.edu.my/id/eprint/2142

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