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https://hdl.handle.net/1822/89555
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Campo DC | Valor | Idioma |
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dc.contributor.author | Matos, Henrique | por |
dc.contributor.author | Santos, Henrique | por |
dc.date.accessioned | 2024-03-14T19:08:35Z | - |
dc.date.available | 2024-03-14T19:08:35Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 1613-0073 | - |
dc.identifier.uri | https://hdl.handle.net/1822/89555 | - |
dc.description.abstract | Object multi-tracking has been a relevant topic for different applications, such as surveillance, mobility, and ambient intelligence. It is particularly challenging when considering open spaces, like Smart Cities, which demand multi-camera solutions with issues like re-identification. In this paper, we describe a framework aiming to provide multi-tracking of people throughout a university campus as part of a larger project (Lab4USpaces) to develop a Smart Campus initiative. Several object detection models and real-time tracking open-source algorithms were compared. The project contemplates a set of low-cost video cameras covering most of the campus, with or without overlapping. After researching different alternatives, the proposed framework uses the YOLOv7 tiny model for object detection, BoT-Sort for multiple object tracking, and Deep Person Reid for re-identification. We also faced challenges concerning the privacy and security of campus users. The multi-tracking system complies with current regulations since no personal identification is ever performed, and no images are stored for longer than necessary for object detection and re-identification. Besides describing the first prototype, this paper discusses some validation tests and describes some potential uses. | por |
dc.description.sponsorship | - (undefined) | por |
dc.language.iso | eng | por |
dc.publisher | CEUR-Ws | por |
dc.rights | openAccess | por |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | por |
dc.subject | Multiple Object Tracking | por |
dc.subject | Object Detection | por |
dc.subject | People Tracking | por |
dc.subject | Re-Identification | por |
dc.subject | Smart Campus | por |
dc.title | People tracking in a smart campus context using multiple cameras | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
oaire.citationVolume | 3601 | por |
dc.date.updated | 2024-03-14T01:18:00Z | - |
dc.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | por |
sdum.export.identifier | 13430 | - |
sdum.journal | CEUR Workshop Proceedings | por |
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Ficheiro | Descrição | Tamanho | Formato | |
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paper_10.pdf | 2,76 MB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons