Smart City Gnosys

Smart city article details

Title Ph.D. Forum: A Study On Real-Time Crowdedness Sensing And Pedestrian Tracking In Multi-Environment
ID_Doc 41975
Authors Huang W.
Year 2024
Published SenSys 2024 - Proceedings of the 2024 ACM Conference on Embedded Networked Sensor Systems
DOI http://dx.doi.org/10.1145/3666025.3699666
Abstract As urban areas continue to expand and populations grow, cities increasingly face challenges related to crowd management. Dense crowds can significantly impact urban traffic, safety, and management, while also creating discomfort in overcrowded spaces. This study investigates how multimodal ubiquitous sensing can be used to create real-time crowdedness sensing and pedestrian tracking in different scenarios. This study proposes new computer vision and wireless signal-based methods for deployment, experiment, evaluation and comparison in open space, semi-open space and closed space, respectively. This research aims to offer reference and guidance for applying crowdedness sensing technologies in various scenarios, which will help in better crowd management and the data sensing of smart cities. © 2024 Copyright is held by the owner/author(s).
Author Keywords Crowdedness Sensing; Multimodal Sensing; Pedestrian Tracking; Smart Cities; Ubiquitous Computing


Similar Articles


Id Similarity Authors Title Published
50724 View0.879De Cock L.; Verstockt S.; Vandeviver C.; Van de Weghe N.Smart Crowd Management: The Data, The Users And The SolutionLeibniz International Proceedings in Informatics, LIPIcs, 240 (2022)
35030 View0.87Das A.; Narayan K.; Chakraborty S.Leveraging Ambient Sensing For The Estimation Of Curiosity-Driven Human CrowdSysCon 2022 - 16th Annual IEEE International Systems Conference, Proceedings (2022)
41420 View0.87Prochazka J.; Plasilova A.Passive Mobile Crowdsensing For Determining The Volume Of Passengers In Public TransportProceedings of 2023 2nd International Conference on Informatics, ICI 2023 (2023)
61740 View0.866Wang Z.; Cao Y.; Jiang K.; Zhou H.; Kang J.; Zhuang Y.; Tian D.; Leung V.C.M.When Crowdsensing Meets Smart Cities: A Comprehensive Survey And New PerspectivesIEEE Communications Surveys and Tutorials, 27, 2 (2025)
48369 View0.861Bessho M.; Sakamura K.Sensing Street-Level Crowd Density By Observing Public Bluetooth Low Energy Advertisements From Contact Tracing Applications2021 IEEE International Smart Cities Conference, ISC2 2021 (2021)
16714 View0.859Bellavista P.; Cardone G.; Corradi A.; Foschini L.; Ianniello R.Crowdsensing In Smart Cities: Technical Challenges, Open Issues, And Emerging Solution GuidelinesHandbook of Research on Social, Economic, and Environmental Sustainability in the Development of Smart Cities (2015)
16655 View0.859Ciabattini L.; Esposito A.; Moghbelan Y.; Forlesi M.; Bruno J.; Zyrianoff I.; Gigli L.; Bononi L.Crosstime: A Mobile Application For Smarter Pedestrian Navigation And Traffic Light AwarenessProceedings - IEEE International Conference on Mobile Data Management (2025)
48371 View0.859Darsena D.; Gelli G.; Iudice I.; Verde F.Sensing Technologies For Crowd Management, Adaptation, And Information Dissemination In Public Transportation Systems: A ReviewIEEE Sensors Journal, 23, 1 (2023)
59717 View0.854Rashid M.T.; Wang D.Unravel: An Anomalistic Crowd Investigation Framework Using Social Airborne SensingConference Proceedings of the IEEE International Performance, Computing, and Communications Conference, 2021-October (2021)
13535 View0.853Schuhback S.; Wischhof L.; Ott J.Cellular Sidelink Enabled Decentralized Pedestrian SensingIEEE Access, 11 (2023)