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Visual Positioning Indoors: Human Eyes vs. Smartphone Cameras

Artificial Intelligence (AI) technologies and their related applications are now developing at a rapid pace. Indoor positioning will be one of the core technologies that enable AI applications because people spend 80% of their time indoors. Humans can locate themselves related to a visually well-def...

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Detalles Bibliográficos
Autores principales: Wu, Dewen, Chen, Ruizhi, Chen, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712973/
https://www.ncbi.nlm.nih.gov/pubmed/29144420
http://dx.doi.org/10.3390/s17112645
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author Wu, Dewen
Chen, Ruizhi
Chen, Liang
author_facet Wu, Dewen
Chen, Ruizhi
Chen, Liang
author_sort Wu, Dewen
collection PubMed
description Artificial Intelligence (AI) technologies and their related applications are now developing at a rapid pace. Indoor positioning will be one of the core technologies that enable AI applications because people spend 80% of their time indoors. Humans can locate themselves related to a visually well-defined object, e.g., a door, based on their visual observations. Can a smartphone camera do a similar job when it points to an object? In this paper, a visual positioning solution was developed based on a single image captured from a smartphone camera pointing to a well-defined object. The smartphone camera simulates the process of human eyes for the purpose of relatively locating themselves against a well-defined object. Extensive experiments were conducted with five types of smartphones on three different indoor settings, including a meeting room, a library, and a reading room. Experimental results shown that the average positioning accuracy of the solution based on five smartphone cameras is 30.6 cm, while that for the human-observed solution with 300 samples from 10 different people is 73.1 cm.
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spelling pubmed-57129732017-12-07 Visual Positioning Indoors: Human Eyes vs. Smartphone Cameras Wu, Dewen Chen, Ruizhi Chen, Liang Sensors (Basel) Article Artificial Intelligence (AI) technologies and their related applications are now developing at a rapid pace. Indoor positioning will be one of the core technologies that enable AI applications because people spend 80% of their time indoors. Humans can locate themselves related to a visually well-defined object, e.g., a door, based on their visual observations. Can a smartphone camera do a similar job when it points to an object? In this paper, a visual positioning solution was developed based on a single image captured from a smartphone camera pointing to a well-defined object. The smartphone camera simulates the process of human eyes for the purpose of relatively locating themselves against a well-defined object. Extensive experiments were conducted with five types of smartphones on three different indoor settings, including a meeting room, a library, and a reading room. Experimental results shown that the average positioning accuracy of the solution based on five smartphone cameras is 30.6 cm, while that for the human-observed solution with 300 samples from 10 different people is 73.1 cm. MDPI 2017-11-16 /pmc/articles/PMC5712973/ /pubmed/29144420 http://dx.doi.org/10.3390/s17112645 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wu, Dewen
Chen, Ruizhi
Chen, Liang
Visual Positioning Indoors: Human Eyes vs. Smartphone Cameras
title Visual Positioning Indoors: Human Eyes vs. Smartphone Cameras
title_full Visual Positioning Indoors: Human Eyes vs. Smartphone Cameras
title_fullStr Visual Positioning Indoors: Human Eyes vs. Smartphone Cameras
title_full_unstemmed Visual Positioning Indoors: Human Eyes vs. Smartphone Cameras
title_short Visual Positioning Indoors: Human Eyes vs. Smartphone Cameras
title_sort visual positioning indoors: human eyes vs. smartphone cameras
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712973/
https://www.ncbi.nlm.nih.gov/pubmed/29144420
http://dx.doi.org/10.3390/s17112645
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