Robust, fine-grained occupancy estimation via combined camera & WiFi indoor localization

We describe the development of a robust, accurate and practically-validated technique for estimating the occupancy count in indoor spaces, based on a combination of WiFi & video sensing. While fusing these two sensing-based inputs is conceptually straightforward, the paper demonstrates and tackl...

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Main Authors: RAVI, Anuradha, MISRA, Archan
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2020
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Online Access:https://ink.library.smu.edu.sg/sis_research/5668
https://ink.library.smu.edu.sg/context/sis_research/article/6672/viewcontent/Robust__Fine_Grained_Occupancy_Estimation_via_Combined_Camera___WiFi_Indoor_Localization_IEEE.pdf
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spelling sg-smu-ink.sis_research-66722021-04-30T07:30:32Z Robust, fine-grained occupancy estimation via combined camera & WiFi indoor localization RAVI, Anuradha MISRA, Archan We describe the development of a robust, accurate and practically-validated technique for estimating the occupancy count in indoor spaces, based on a combination of WiFi & video sensing. While fusing these two sensing-based inputs is conceptually straightforward, the paper demonstrates and tackles the complexity that arises from several practical artefacts, such as (i) over-counting when a single individual uses multiple WiFi devices and under-counting when the individual has no such device; (ii) corresponding errors in image analysis due to real-world artefacts, such as occlusion, and (iii) the variable errors in mapping image bounding boxes (which can include multiple possible types of human views: fhead, torso, full-bodyg) to location coordinates. We develop statistical techniques to overcome these practical challenges, and finally propose a novel fusion algorithm, based on inexact bipartite matching of these two streams of independent estimates, to estimate the occupancy in complex, multi-inhabitant indoor spaces (such as university labs). We experimentally demonstrate that this estimation technique is robust and accurate, achieving less than 20% error, in an approx. 85m2 lab space (with the error staying below 30% in a smaller 25m2 area), across a wide variety of occupancy conditions. 2020-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5668 info:doi/10.1109/MASS50613.2020.00074 https://ink.library.smu.edu.sg/context/sis_research/article/6672/viewcontent/Robust__Fine_Grained_Occupancy_Estimation_via_Combined_Camera___WiFi_Indoor_Localization_IEEE.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University indoor localization occupancy estimation camera occupancy RADAR occupancy Numerical Analysis and Scientific Computing Software Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic indoor localization
occupancy estimation
camera occupancy
RADAR occupancy
Numerical Analysis and Scientific Computing
Software Engineering
spellingShingle indoor localization
occupancy estimation
camera occupancy
RADAR occupancy
Numerical Analysis and Scientific Computing
Software Engineering
RAVI, Anuradha
MISRA, Archan
Robust, fine-grained occupancy estimation via combined camera & WiFi indoor localization
description We describe the development of a robust, accurate and practically-validated technique for estimating the occupancy count in indoor spaces, based on a combination of WiFi & video sensing. While fusing these two sensing-based inputs is conceptually straightforward, the paper demonstrates and tackles the complexity that arises from several practical artefacts, such as (i) over-counting when a single individual uses multiple WiFi devices and under-counting when the individual has no such device; (ii) corresponding errors in image analysis due to real-world artefacts, such as occlusion, and (iii) the variable errors in mapping image bounding boxes (which can include multiple possible types of human views: fhead, torso, full-bodyg) to location coordinates. We develop statistical techniques to overcome these practical challenges, and finally propose a novel fusion algorithm, based on inexact bipartite matching of these two streams of independent estimates, to estimate the occupancy in complex, multi-inhabitant indoor spaces (such as university labs). We experimentally demonstrate that this estimation technique is robust and accurate, achieving less than 20% error, in an approx. 85m2 lab space (with the error staying below 30% in a smaller 25m2 area), across a wide variety of occupancy conditions.
format text
author RAVI, Anuradha
MISRA, Archan
author_facet RAVI, Anuradha
MISRA, Archan
author_sort RAVI, Anuradha
title Robust, fine-grained occupancy estimation via combined camera & WiFi indoor localization
title_short Robust, fine-grained occupancy estimation via combined camera & WiFi indoor localization
title_full Robust, fine-grained occupancy estimation via combined camera & WiFi indoor localization
title_fullStr Robust, fine-grained occupancy estimation via combined camera & WiFi indoor localization
title_full_unstemmed Robust, fine-grained occupancy estimation via combined camera & WiFi indoor localization
title_sort robust, fine-grained occupancy estimation via combined camera & wifi indoor localization
publisher Institutional Knowledge at Singapore Management University
publishDate 2020
url https://ink.library.smu.edu.sg/sis_research/5668
https://ink.library.smu.edu.sg/context/sis_research/article/6672/viewcontent/Robust__Fine_Grained_Occupancy_Estimation_via_Combined_Camera___WiFi_Indoor_Localization_IEEE.pdf
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