Object Recognition Models for Indoor Users’ Location
- Autores
- Borrelli, Franco Martín; Challiol, Cecilia
- Año de publicación
- 2024
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- 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.
- Materia
-
Ciencias de la Computación e Información
Object Recognition Models
Indoor Location
User Location
Lightweight Networks - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/4.0/
- Repositorio
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- Institución
- Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
- OAI Identificador
- oai:digital.cic.gba.gob.ar:11746/12347
Ver los metadatos del registro completo
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Object Recognition Models for Indoor Users’ LocationBorrelli, Franco MartínChalliol, CeciliaCiencias de la Computación e InformaciónObject Recognition ModelsIndoor LocationUser LocationLightweight NetworksDespite 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.2024info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttps://digital.cic.gba.gob.ar/handle/11746/12347enginfo:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-031-70807-7_3info:eu-repo/semantics/altIdentifier/isbn/978-3-031-70807-7info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/reponame:CIC Digital (CICBA)instname:Comisión de Investigaciones Científicas de la Provincia de Buenos Airesinstacron:CICBA2025-10-23T11:14:12Zoai:digital.cic.gba.gob.ar:11746/12347Institucionalhttp://digital.cic.gba.gob.arOrganismo científico-tecnológicoNo correspondehttp://digital.cic.gba.gob.ar/oai/snrdmarisa.degiusti@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:94412025-10-23 11:14:12.451CIC Digital (CICBA) - Comisión de Investigaciones Científicas de la Provincia de Buenos Airesfalse |
| dc.title.none.fl_str_mv |
Object Recognition Models for Indoor Users’ Location |
| title |
Object Recognition Models for Indoor Users’ Location |
| spellingShingle |
Object Recognition Models for Indoor Users’ Location Borrelli, Franco Martín Ciencias de la Computación e Información Object Recognition Models Indoor Location User Location Lightweight Networks |
| title_short |
Object Recognition Models for Indoor Users’ Location |
| title_full |
Object Recognition Models for Indoor Users’ Location |
| title_fullStr |
Object Recognition Models for Indoor Users’ Location |
| title_full_unstemmed |
Object Recognition Models for Indoor Users’ Location |
| title_sort |
Object Recognition Models for Indoor Users’ Location |
| dc.creator.none.fl_str_mv |
Borrelli, Franco Martín Challiol, Cecilia |
| author |
Borrelli, Franco Martín |
| author_facet |
Borrelli, Franco Martín Challiol, Cecilia |
| author_role |
author |
| author2 |
Challiol, Cecilia |
| author2_role |
author |
| dc.subject.none.fl_str_mv |
Ciencias de la Computación e Información Object Recognition Models Indoor Location User Location Lightweight Networks |
| topic |
Ciencias de la Computación e Información Object Recognition Models Indoor Location User Location Lightweight Networks |
| dc.description.none.fl_txt_mv |
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. |
| description |
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. |
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2024 |
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2024 |
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eng |
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