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Authors: Wing-Fat Cheng 1 ; Man-Ching Yuen 2 and Yuk-Chun So 3

Affiliations: 1 Department of Information Technology, Vocational Training Council, Hong Kong ; 2 iFREE GROUP Innovation and Research Centre, Department of Applied Data Science, Hong Kong Shue Yan University, Hong Kong ; 3 Department of Information Technology, University of the West of England Bristol, U.K.

Keyword(s): Convolutional Neural Network, Aerial Image, Crowd Size Estimation.

Abstract: Using image and video to conduct crowd analysis in public places is an effective tool to establish situational awareness. Currently, the gap between different organizations on crowd counting differs greatly. Many research works investigated into utilizing image recognition technology to provide a fair estimation of the crowd count. In this paper, we propose a convolutional neural network model on aerial image analysis to learn to estimate crowd size. To find out the requirements of the efficient and reliable crowd size estimation system, we also investigate current approaches in crowd size estimation, such as regression, CNN and by-detention with image recognition technology. Our work allows the event organizers to get a fair description of the crowd behaviors. The main contribution of this paper is the application of CNN for solving the problem of crowd size estimation.

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Paper citation in several formats:
Cheng, W.; Yuen, M. and So, Y. (2022). Learning to Estimate Crowd Size by Applying Convolutional Neural Network to Aerial Imaging Analysis. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KDIR; ISBN 978-989-758-614-9; ISSN 2184-3228, SciTePress, pages 237-242. DOI: 10.5220/0011542500003335

@conference{kdir22,
author={Wing{-}Fat Cheng. and Man{-}Ching Yuen. and Yuk{-}Chun So.},
title={Learning to Estimate Crowd Size by Applying Convolutional Neural Network to Aerial Imaging Analysis},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KDIR},
year={2022},
pages={237-242},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011542500003335},
isbn={978-989-758-614-9},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - KDIR
TI - Learning to Estimate Crowd Size by Applying Convolutional Neural Network to Aerial Imaging Analysis
SN - 978-989-758-614-9
IS - 2184-3228
AU - Cheng, W.
AU - Yuen, M.
AU - So, Y.
PY - 2022
SP - 237
EP - 242
DO - 10.5220/0011542500003335
PB - SciTePress

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