Object Recognition Models for Indoor Users’ Location
| cic.institucionOrigen | Laboratorio de Investigación y Formación en Informática Avanzada (LIFIA) | |
| cic.isFulltext | SI | |
| cic.isPeerReviewed | SI | |
| cic.lugarDesarrollo | Laboratorio de Investigación y Formación en Informática Avanzada (LIFIA) | |
| cic.parentType | Objeto de conferencia | |
| cic.version | Aceptada | |
| dc.date.accessioned | 2024-10-29T12:13:42Z | |
| dc.date.available | 2024-10-29T12:13:42Z | |
| dc.identifier.uri | https://digital.cic.gba.gob.ar/handle/11746/12347 | |
| dc.title | Object Recognition Models for Indoor Users’ Location | en |
| dc.type | Documento de conferencia | |
| dcterms.abstract | Despite technological advances, precise positioning within buildings remains a considerable challenge. In this context, the present paper explores the research of user location in indoor spaces, embracing object recognition models executed directly on mobile devices. Our proposal is based on designing a generic solution architecture adaptable to any physical environment, enabling the definition and usage of relevant generic objects within the environment to determine the users' current location. This proposal uses Computer Vision, employing object recognition models for positioning. This kind of indoor positioning benefits from the growth of smartphones' functionalities and capabilities, thus avoiding the need to install additional infrastructures in physical spaces. A specific implementation of this architecture for React Native is presented, using the TensorFlow platform to support object recognition. This implementation allows demonstrating how this positioning works through concrete use cases. In addition, some lessons learned are discussed, which we hope will contribute to this topic. | en |
| dcterms.creator.author | Borrelli, Franco Martín | |
| dcterms.creator.author | Challiol, Cecilia | |
| dcterms.identifier.other | DOI: 10.1007/978-3-031-70807-7_3 | |
| dcterms.identifier.other | ISBN: 978-3-031-70807-7 | |
| dcterms.isPartOf.item | Cloud Computing, Big Data and Emerging Topics. JCC-BD&ET 2024. Communications in Computer and Information Science, vol 2189 | |
| dcterms.isPartOf.series | 12 Jornadas de Cloud Computing, Big Data & Emerging Topics (La Plata, 25 al 27 de junio de 2024) | |
| dcterms.issued | 2024 | |
| dcterms.language | Inglés | |
| dcterms.license | Attribution-NonCommercial-ShareAlike 4.0 International (BY-NC-SA 4.0) | |
| dcterms.subject | Object Recognition Models | en |
| dcterms.subject | Indoor Location | en |
| dcterms.subject | User Location | en |
| dcterms.subject | Lightweight Networks | en |
| dcterms.subject.materia | Ciencias de la Computación e Información |
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