Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/109433
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dc.contributorDepartment of Electrical and Electronic Engineering-
dc.contributorOtto Poon Charitable Foundation Smart Cities Research Institute-
dc.contributorDepartment of Civil and Environmental Engineering-
dc.creatorWang, Y-
dc.creatorHo, IWH-
dc.creatorWang, Y-
dc.date.accessioned2024-10-18T06:10:21Z-
dc.date.available2024-10-18T06:10:21Z-
dc.identifier.urihttp://hdl.handle.net/10397/109433-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© The Author(s) 2023. This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).en_US
dc.rightsThe following publication Y. Wang, I. W. -H. Ho and Y. Wang, "Real-Time Intersection Vehicle Turning Movement Counts from Live UAV Video Stream Using Multiple Object Tracking," in Journal of Intelligent and Connected Vehicles, vol. 6, no. 3, pp. 149-160, September 2023 is available at https://doi.org/10.26599/JICV.2023.9210014.en_US
dc.subjectMulti objects trackingen_US
dc.subjectReal-timeen_US
dc.subjectTurning movement countsen_US
dc.subjectUnmanned aerial vehiclesen_US
dc.titleReal-time intersection vehicle turning movement counts from live UAV video stream using multiple object trackingen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage149-
dc.identifier.epage160-
dc.identifier.volume6-
dc.identifier.issue3-
dc.identifier.doi10.26599/JICV.2023.9210014-
dcterms.abstractThe intelligent transportation system (ITS) is committed to ensuring safe and effective next-generation traffic throughout a city. However, such efficient operation on urban traffic networks needs the support of big traffic data, especially Turning Movement Counts (TMC) at intersections. Generally, TMC data are more challenging to collect due to labor cost and accuracy problems. In this paper, we leverage the capabilities of Unmanned Aerial Vehicles (UAV) to collect real-time TMC data in a cost-efficient way. We proposed a real-time TMC data collection framework based on a live video stream. The vehicle tracking capability is boosted by multiple object tracking based on tracking by detection. In addition, a challenging case study was conducted, and our results demonstrate the feasibility and robustness of the proposed TMC data collection framework. Specifically, with a GTX 1650 graphics card, about 10 FPS can be achieved in real-time for the TMC data collection. The overall accuracy is 91.93%, and the best case is over 98% accurate. In the context of miscounting, the major reason is due to ID switching caused by background occlusion. The proposed framework is expected to provide real-time data for traffic capacity analysis and advanced traffic simulation such as digital twins.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of intelligent and connected vehicles, Sept 2023, v. 6, no. 3, p. 149-160-
dcterms.isPartOfJournal of intelligent and connected vehicles-
dcterms.issued2023-09-
dc.identifier.scopus2-s2.0-85183076418-
dc.identifier.eissn2399-9802-
dc.description.validate202410 bcch-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberCDCF_2023-2024en_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextResearch Impact Fund, University Grant Committee (UGC) of the Hong Kong Special Administrative Region (HKSAR), China; Otto Poon Charitable Foundation Smart Cities Research Institute (Q-CDAS)en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryCCen_US
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