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Building Multi-occupancy Analysis and Visualization Through Data Intensive Processing

Authors :
Spiridon D. Likothanassis
Stelios Krinidis
Dimitrios Tzovaras
Pantelis Tropios
Dimosthenis Ioannidis
University of Patras [Patras]
Centre for Research and Technology Hellas [Athènes] (CERTH)
Lazaros Iliadis
Ilias Maglogiannis
TC 12
WG 12.5
Source :
Artificial Intelligence Applications and Innovations, IFIP Advances in Information and Communication Technology, 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), 12th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2016, Thessaloniki, Greece. pp.587-599, ⟨10.1007/978-3-319-44944-9_52⟩, IFIP Advances in Information and Communication Technology-Artificial Intelligence Applications and Innovations, IFIP Advances in Information and Communication Technology ISBN: 9783319449432, AIAI
Publication Year :
2016

Abstract

Part 11: New Methods and Tools for Big Data Wokshop (MT4BD); International audience; A novel Building Multi-occupancy Analysis & Visualization through Data Intensive Processing techniques is going to be presented in this paper. Building occupancy monitoring plays an important role in increasing energy efficiency and provides useful semantic information about the usage of different spaces and building performance generally. In this paper the occupancy extraction subsystem is constituted by a collection of depth image cameras and a multi-sensorial cloud (utilizing big data from various sensor types) in order to extract the occupancy per space. Furthermore, a number of novel visual analytics techniques allow the end-users to process big data in different temporal resolutions in a compact and comprehensive way taking into account properties of human cognition and perception, assisting them to detect patterns that may be difficult to be detected otherwise. The proposed building occupancy analysis system has been tested and applied to various spaces of CERTH premises with different characteristics in a real-life testbed environment.

Details

ISBN :
978-3-319-44943-2
978-3-319-44944-9
ISSN :
18684238 and 1868422X
ISBNs :
9783319449432 and 9783319449449
Database :
OpenAIRE
Journal :
Artificial Intelligence Applications and Innovations
Accession number :
edsair.doi.dedup.....b735a30793dc5ce154372497941a3944
Full Text :
https://doi.org/10.1007/978-3-319-44944-9_52