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Conference Paper/Proceeding/Abstract 948 views 174 downloads

Labeling subtle conversational interactions within the CONVERSE dataset

Mike Edwards Orcid Logo, Jingjing Deng, Xianghua Xie Orcid Logo

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Pages: 140 - 145

Swansea University Authors: Mike Edwards Orcid Logo, Jingjing Deng, Xianghua Xie Orcid Logo

DOI (Published version): 10.1109/percomw.2017.7917547

Abstract

The field of Human Action Recognition has ex- panded greatly in previous years, exploring actions and inter- actions between individuals via the use of appearance and depth based pose information. There are numerous datasets that display action classes composed of behaviors that are well defined by...

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Published in: 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
ISBN: 978-1-5090-4339-2 978-1-5090-4338-5
Published: IEEE 2017
URI: https://cronfa.swan.ac.uk/Record/cronfa33105
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spelling 2021-09-16T16:35:58.4280114 v2 33105 2017-04-27 Labeling subtle conversational interactions within the CONVERSE dataset 684864a1ce01c3d774e83ed55e41770e 0000-0003-3367-969X Mike Edwards Mike Edwards true false 6f6d01d585363d6dc1622640bb4fcb3f Jingjing Deng Jingjing Deng true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2017-04-27 SCS The field of Human Action Recognition has ex- panded greatly in previous years, exploring actions and inter- actions between individuals via the use of appearance and depth based pose information. There are numerous datasets that display action classes composed of behaviors that are well defined by their key poses, such as ‘kicking’ and ‘punching’. The CONVERSE dataset presents conversational interaction classes that show little explicit relation to the poses and gestures they exhibit. Such a complex and subtle set of interactions is a novel challenge to the Human Action Recognition community, and one that will push the cutting edge of the field in both machine learning and the understanding of human actions. CONVERSE contains recordings of two person interactions from 7 conversational scenarios, represented as sequences of human skeletal poses captured by the Kinect depth sensor. In this study we discuss a method providing ground truth labelling for the set, and the complexity that comes with defining such annotation. The CONVERSE dataset it made available online. Conference Paper/Proceeding/Abstract 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) 140 145 IEEE 978-1-5090-4339-2 978-1-5090-4338-5 4 5 2017 2017-05-04 10.1109/percomw.2017.7917547 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2021-09-16T16:35:58.4280114 2017-04-27T09:51:42.8053849 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Mike Edwards 0000-0003-3367-969X 1 Jingjing Deng 2 Xianghua Xie 0000-0002-2701-8660 3 0033105-27042017095227.pdf mk17arduous.pdf 2017-04-27T09:52:27.9370000 Output 8489808 application/pdf Accepted Manuscript true 2017-03-01T00:00:00.0000000 true eng
title Labeling subtle conversational interactions within the CONVERSE dataset
spellingShingle Labeling subtle conversational interactions within the CONVERSE dataset
Mike Edwards
Jingjing Deng
Xianghua Xie
title_short Labeling subtle conversational interactions within the CONVERSE dataset
title_full Labeling subtle conversational interactions within the CONVERSE dataset
title_fullStr Labeling subtle conversational interactions within the CONVERSE dataset
title_full_unstemmed Labeling subtle conversational interactions within the CONVERSE dataset
title_sort Labeling subtle conversational interactions within the CONVERSE dataset
author_id_str_mv 684864a1ce01c3d774e83ed55e41770e
6f6d01d585363d6dc1622640bb4fcb3f
b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv 684864a1ce01c3d774e83ed55e41770e_***_Mike Edwards
6f6d01d585363d6dc1622640bb4fcb3f_***_Jingjing Deng
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Mike Edwards
Jingjing Deng
Xianghua Xie
author2 Mike Edwards
Jingjing Deng
Xianghua Xie
format Conference Paper/Proceeding/Abstract
container_title 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
container_start_page 140
publishDate 2017
institution Swansea University
isbn 978-1-5090-4339-2
978-1-5090-4338-5
doi_str_mv 10.1109/percomw.2017.7917547
publisher IEEE
college_str Faculty of Science and Engineering
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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
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description The field of Human Action Recognition has ex- panded greatly in previous years, exploring actions and inter- actions between individuals via the use of appearance and depth based pose information. There are numerous datasets that display action classes composed of behaviors that are well defined by their key poses, such as ‘kicking’ and ‘punching’. The CONVERSE dataset presents conversational interaction classes that show little explicit relation to the poses and gestures they exhibit. Such a complex and subtle set of interactions is a novel challenge to the Human Action Recognition community, and one that will push the cutting edge of the field in both machine learning and the understanding of human actions. CONVERSE contains recordings of two person interactions from 7 conversational scenarios, represented as sequences of human skeletal poses captured by the Kinect depth sensor. In this study we discuss a method providing ground truth labelling for the set, and the complexity that comes with defining such annotation. The CONVERSE dataset it made available online.
published_date 2017-05-04T03:40:44Z
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score 11.011735