Occupancy estimation and people flow prediction in smart environments

Two related problems have been analysed. Inthe one hand, the problem of detecting people by using indoor climate monitoring infrastructure is analysed, while on the other hand, predicting the amount of people in one space based on some criteria is studied. These two problems are grouped in the Ambie...

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Detalles Bibliográficos
Autor: Saralegui Vallejo, Unai
Tipo de recurso: tesis de maestría
Fecha de publicación:2017
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/22637
Acceso en línea:http://hdl.handle.net/10810/22637
Access Level:acceso abierto
Palabra clave:supervised learning
ambient intelligence
smart building and cities
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spelling Occupancy estimation and people flow prediction in smart environmentsSaralegui Vallejo, Unaisupervised learningambient intelligencesmart building and citiesTwo related problems have been analysed. Inthe one hand, the problem of detecting people by using indoor climate monitoring infrastructure is analysed, while on the other hand, predicting the amount of people in one space based on some criteria is studied. These two problems are grouped in the Ambient Intelligence (AmI) research field. In the smart building and cities (SBC) are avarious research paths are gaining increasing attention, especially with the advances in the Internet of Things (IoT) paradigm and the Big Data analysis. Some hot topics in this research field include city security, surveillance, providing more efficient public services, event scheduling, etc. The analysed problems are introduced with the state of the art for each one, current research paths and possible limitations of the proposed methods are also mentioned. In the last section of this chapter some supervised learning algorithms used in this work are introduced and explained.Muguerza Rivero, Javier FranciscoArbelaiz Gallego, OlatzAntón, Miguel Ángel201720172017info:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10810/22637reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/3.0/es/Atribución-NoComercial-CompartirIgual 3.0 Españaoai:addi.ehu.eus:10810/226372026-06-18T09:23:17Z
dc.title.none.fl_str_mv Occupancy estimation and people flow prediction in smart environments
title Occupancy estimation and people flow prediction in smart environments
spellingShingle Occupancy estimation and people flow prediction in smart environments
Saralegui Vallejo, Unai
supervised learning
ambient intelligence
smart building and cities
title_short Occupancy estimation and people flow prediction in smart environments
title_full Occupancy estimation and people flow prediction in smart environments
title_fullStr Occupancy estimation and people flow prediction in smart environments
title_full_unstemmed Occupancy estimation and people flow prediction in smart environments
title_sort Occupancy estimation and people flow prediction in smart environments
dc.creator.none.fl_str_mv Saralegui Vallejo, Unai
author Saralegui Vallejo, Unai
author_facet Saralegui Vallejo, Unai
author_role author
dc.contributor.none.fl_str_mv Muguerza Rivero, Javier Francisco
Arbelaiz Gallego, Olatz
Antón, Miguel Ángel
dc.subject.none.fl_str_mv supervised learning
ambient intelligence
smart building and cities
topic supervised learning
ambient intelligence
smart building and cities
description Two related problems have been analysed. Inthe one hand, the problem of detecting people by using indoor climate monitoring infrastructure is analysed, while on the other hand, predicting the amount of people in one space based on some criteria is studied. These two problems are grouped in the Ambient Intelligence (AmI) research field. In the smart building and cities (SBC) are avarious research paths are gaining increasing attention, especially with the advances in the Internet of Things (IoT) paradigm and the Big Data analysis. Some hot topics in this research field include city security, surveillance, providing more efficient public services, event scheduling, etc. The analysed problems are introduced with the state of the art for each one, current research paths and possible limitations of the proposed methods are also mentioned. In the last section of this chapter some supervised learning algorithms used in this work are introduced and explained.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017
2017
dc.type.none.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/22637
url http://hdl.handle.net/10810/22637
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/3.0/es/
Atribución-NoComercial-CompartirIgual 3.0 España
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/3.0/es/
Atribución-NoComercial-CompartirIgual 3.0 España
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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