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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| Formato: | tesis de maestría |
| Fecha de publicación: | 2017 |
| País: | España |
| Recursos: | Universidad del País Vasco |
| Repositorio: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:addi.ehu.eus:10810/22637 |
| Acesso em linha: | http://hdl.handle.net/10810/22637 |
| Access Level: | acceso abierto |
| Palavra-chave: | supervised learning ambient intelligence smart building and cities |
| Resumo: | 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. |
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