@inproceedings{9d1424d447c14c9695a3117bf0051b22,
title = "Experiments of Crowd Detection for Crowd Digital Twins",
abstract = "The development of a crowd digital twin offers significant potential for enhancing public safety, urban planning, and event management. A key challenge in creating such a digital twin lies in the efficient and accurate acquisition of crowd-related data, particularly through object detection models deployed on resource-constrained devices. Through a series of experiments, we compare TinyML and Edge approaches in terms of detection accuracy, inferencing time, and resource utilization. Our findings highlight the trade-offs inherent in selecting detection models for crowd digital twin applications, underscoring the importance of aligning model choice with specific deployment needs.",
keywords = "crowd digital twin, crowd management, crowd safety, edge ml, human crowds detection, tinyml",
author = "Chong, \{Kuan Pok\} and Lai, \{Chon Hou\} and Weibo Ling and Zhuoqian Lu and Rui Wang and Ruoqi Wang and Yanjun Yu and Alex Testa and Lam, \{Chan Tong\} and Tang, \{Su Kit\} and Giovanni Delnevo and Roberto Casadei and Roberto Girau and Silvia Mirri",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 22nd IEEE Consumer Communications and Networking Conference, CCNC 2025 ; Conference date: 10-01-2025 Through 13-01-2025",
year = "2025",
doi = "10.1109/CCNC54725.2025.10976164",
language = "English",
series = "Proceedings - IEEE Consumer Communications and Networking Conference, CCNC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 IEEE 22nd Consumer Communications and Networking Conference, CCNC 2025",
address = "United States",
}