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Multiple Human Tracking in RGB-D Data: A Survey

Massimo Camplani, Adeline Paiement, Majid Mirmehdi, Dima Damen, Sion Hannuuna, Tilo Burghardt, Lili Tao

IET Computer Vision

Swansea University Author: Adeline Paiement

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DOI (Published version): 10.1049/iet-cvi.2016.0178

Abstract

Multiple human tracking (MHT) is a fundamental task in many computer visionapplications. Appearance-based approaches, primarily formulated on RGB data,are constrained and affected by problems arising from occlusions and/orillumination variations. In recent years, the arrival of cheap RGB-Depth(RGB-D...

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Published in: IET Computer Vision
ISSN: 1751-9632 1751-9640
Published: 2016
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URI: https://cronfa.swan.ac.uk/Record/cronfa31412
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spelling 2017-12-11T14:26:56.6727182 v2 31412 2016-12-09 Multiple Human Tracking in RGB-D Data: A Survey f50adf4186d930e3a2a0f9a6d643cf53 Adeline Paiement Adeline Paiement true false 2016-12-09 FGHSS Multiple human tracking (MHT) is a fundamental task in many computer visionapplications. Appearance-based approaches, primarily formulated on RGB data,are constrained and affected by problems arising from occlusions and/orillumination variations. In recent years, the arrival of cheap RGB-Depth(RGB-D) devices has {led} to many new approaches to MHT, and many of theseintegrate color and depth cues to improve each and every stage of the process.In this survey, we present the common processing pipeline of these methods andreview their methodology based (a) on how they implement this pipeline and (b)on what role depth plays within each stage of it. We identify and introduceexisting, publicly available, benchmark datasets and software resources thatfuse color and depth data for MHT. Finally, we present a brief comparativeevaluation of the performance of those works that have applied their methods tothese datasets. Journal Article IET Computer Vision 1751-9632 1751-9640 12 12 2016 2016-12-12 10.1049/iet-cvi.2016.0178 COLLEGE NANME Humanities and Social Sciences - Faculty COLLEGE CODE FGHSS Swansea University 2017-12-11T14:26:56.6727182 2016-12-09T12:16:16.4173922 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Massimo Camplani 1 Adeline Paiement 2 Majid Mirmehdi 3 Dima Damen 4 Sion Hannuuna 5 Tilo Burghardt 6 Lili Tao 7 0031412-25102017160641.pdf IET-CVIv2.pdf 2017-10-25T16:06:41.4970000 Output 4211992 application/pdf Version of Record true 2017-10-25T00:00:00.0000000 This is an open access article published by the IET under the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/3.0/) true eng
title Multiple Human Tracking in RGB-D Data: A Survey
spellingShingle Multiple Human Tracking in RGB-D Data: A Survey
Adeline Paiement
title_short Multiple Human Tracking in RGB-D Data: A Survey
title_full Multiple Human Tracking in RGB-D Data: A Survey
title_fullStr Multiple Human Tracking in RGB-D Data: A Survey
title_full_unstemmed Multiple Human Tracking in RGB-D Data: A Survey
title_sort Multiple Human Tracking in RGB-D Data: A Survey
author_id_str_mv f50adf4186d930e3a2a0f9a6d643cf53
author_id_fullname_str_mv f50adf4186d930e3a2a0f9a6d643cf53_***_Adeline Paiement
author Adeline Paiement
author2 Massimo Camplani
Adeline Paiement
Majid Mirmehdi
Dima Damen
Sion Hannuuna
Tilo Burghardt
Lili Tao
format Journal article
container_title IET Computer Vision
publishDate 2016
institution Swansea University
issn 1751-9632
1751-9640
doi_str_mv 10.1049/iet-cvi.2016.0178
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Multiple human tracking (MHT) is a fundamental task in many computer visionapplications. Appearance-based approaches, primarily formulated on RGB data,are constrained and affected by problems arising from occlusions and/orillumination variations. In recent years, the arrival of cheap RGB-Depth(RGB-D) devices has {led} to many new approaches to MHT, and many of theseintegrate color and depth cues to improve each and every stage of the process.In this survey, we present the common processing pipeline of these methods andreview their methodology based (a) on how they implement this pipeline and (b)on what role depth plays within each stage of it. We identify and introduceexisting, publicly available, benchmark datasets and software resources thatfuse color and depth data for MHT. Finally, we present a brief comparativeevaluation of the performance of those works that have applied their methods tothese datasets.
published_date 2016-12-12T03:38:22Z
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score 11.036006

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