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The paper proposes a real-time crowd counting system based on machine learning, using a convolutional neural network (CNN) deep learning model to process images and video streams. The system targets robust inference across changes in lighting and crowd density, adapting to different camera perspectives through training on diverse datasets. 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Helini 1*, B. Niharika2, B. Tejaswini3, D. Shriya4, K. Anjali5  \n1, 2,3,4,5Department of Information Technology, Vignan’s Institute of Management and  \nTechnology, Gjatkesar, Hyderabad, India  \n[Email:](Email: kolanhelinireddy@gmail.com1)[ kolanhelinireddy@gmail.com](Email: kolanhelinireddy@gmail.com1)[1](Email: kolanhelinireddy@gmail.com1)*, [bniharikareddy67@gmail.com](bniharikareddy67@gmail.com2)[2](bniharikareddy67@gmail.com2),  \n[burratejaswini57@gmail.com](burratejaswini57@gmail.com3)[3](burratejaswini57@gmail.com3), [degaonshriyareddy@gmail.com](degaonshriyareddy@gmail.com4)[4](degaonshriyareddy@gmail.com4), [1606249anjali@gmail.com](1606249anjali@gmail.com5)[5](1606249anjali@gmail.com5)  \nAbstract  \nCrowd 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.  \nKeywords  \nConvolutional Neural Networks (CNN), Deep Learning; Image Processing, Video Analytics,  \nYOLO.  \nIntroduction  \nConcerts, festivals, sports games, and other entertainment events often bring about crowding. Researchers in the field of computer vision find crowd behavior analysis to be particularly interesting and active. Depending on the situation, crowds are groups of individuals often found in a certain area. The people in a temple will tend to be different than the people in a shopping area. A crowd is made up of people who happen to be in the same place at the same time. The growing human population is often associated with more crowded places. Therefore, to check on these occasions, it is important to have many CCTV cameras in place to supervise the crowd.  \nSince the human eye cannot cover all the cameras at once, using only patrol guard is impractical. For this reason, technology should be developed for constant monitoring of crowds over a long time. Some of the issues in detecting events automatically are having several events  \nSubmission: 11 March 2025; Acceptance: 1 4 June 2025; Available Online: June 2025  \nCopyright: © 2025. All the authors listed in this paper. The distribution, reproduction, and any other usage of the content of this paper is permitted, with credit given to all the author(s) and copyright owner(s) in accordance to common academic practice. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, as stated in the website: [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nJOURNAL OF DATA SCIENCE | Vol.2025, Issue 1, No.3 eISSN:2805-5160  \noccur at once, dealing with much data at once, obstructions, and detecting events instantly. To keep the public safe, public authorities must watch surveillance recordings to identify dangerous crowds and their actions. Algorithms used for viewing crowds using computers usually belong to one of three main areas: people counting, people tracking, and studying crowd behavior. For people counting, we estimate the number of visitors to ensure the facility is not too crowded.  \nPeople counting allows for the estimation of people in","cbCaimPrCSEEry1G","https://ap.wps.com/l/cbCaimPrCSEEry1G","pdf",141216,"English","# Abstract\n# Introduction\n# Materials and Methods\n## Dataset Collection\n## (Further methods not shown)","[{\"question\":\"What problem does the system address?\",\"answer\":\"It addresses crowd counting for public safety, event management, and urban planning by estimating the number of people in a scene in real time.\"},{\"question\":\"Which model and data types does the approach use?\",\"answer\":\"The approach uses a CNN-based deep learning model to process images and video streams and count individuals in diverse environments.\"},{\"question\":\"How is the system made robust across different conditions?\",\"answer\":\"It is trained on a diverse set of datasets covering crowded events, open spaces, and public gatherings, improving performance under varying lighting, density, and camera perspectives.\"}]","Real Time Crowd Counting System Using Machine Learning | PDF",25]