Data2MV - A user behaviour dataset for multi-view scenarios

Bibliographic Details
Main Author: Soares da Costa, Tiago
Publication Date: 2023
Other Authors: Andrade, Maria Teresa, Viana, Paula, Silva, Nuno Castro
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10400.22/24730
Summary: The Data2MV dataset contains gaze fixation data obtained through experimental procedures from a total of 45 partic- ipants using an Intel RealSense F200 camera module and seven different video playlists. Each of the playlists had an approximate duration of 20 minutes and was viewed at least 17 times, with raw tracking data being recorded with a 0.05 second interval. The Data2MV dataset encompasses a total of 1.0 0 0.845 gaze fixations, gathered across a total of 128 exper- iments. It is also composed of 68.393 image frames, extracted from each of the 6 videos selected for these experiments, and an equal quantity of saliency maps, generated from aggregate fixation data. Software tools to obtain saliency maps and generate complementary plots are also provided as an open- source software package. The Data2MV dataset was publicly released to the research community on Mendeley Data and constitutes an important contribution to reduce the current scarcity of such data, particularly in immersive, multi-view streaming scenarios.
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spelling Data2MV - A user behaviour dataset for multi-view scenariosMultimedia; Multi-view; Head-tracking; Adaptive streaming; View prediction; Deep learningThe Data2MV dataset contains gaze fixation data obtained through experimental procedures from a total of 45 partic- ipants using an Intel RealSense F200 camera module and seven different video playlists. Each of the playlists had an approximate duration of 20 minutes and was viewed at least 17 times, with raw tracking data being recorded with a 0.05 second interval. The Data2MV dataset encompasses a total of 1.0 0 0.845 gaze fixations, gathered across a total of 128 exper- iments. It is also composed of 68.393 image frames, extracted from each of the 6 videos selected for these experiments, and an equal quantity of saliency maps, generated from aggregate fixation data. Software tools to obtain saliency maps and generate complementary plots are also provided as an open- source software package. The Data2MV dataset was publicly released to the research community on Mendeley Data and constitutes an important contribution to reduce the current scarcity of such data, particularly in immersive, multi-view streaming scenarios.ElsevierREPOSITÓRIO P.PORTOSoares da Costa, TiagoAndrade, Maria TeresaViana, PaulaSilva, Nuno Castro2024-01-29T08:24:24Z2023-10-202023-10-20T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/24730eng10.1016/j.dib.2023.109702info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-04-02T03:03:12Zoai:recipp.ipp.pt:10400.22/24730Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T00:37:21.628883Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Data2MV - A user behaviour dataset for multi-view scenarios
title Data2MV - A user behaviour dataset for multi-view scenarios
spellingShingle Data2MV - A user behaviour dataset for multi-view scenarios
Soares da Costa, Tiago
Multimedia; Multi-view; Head-tracking; Adaptive streaming; View prediction; Deep learning
title_short Data2MV - A user behaviour dataset for multi-view scenarios
title_full Data2MV - A user behaviour dataset for multi-view scenarios
title_fullStr Data2MV - A user behaviour dataset for multi-view scenarios
title_full_unstemmed Data2MV - A user behaviour dataset for multi-view scenarios
title_sort Data2MV - A user behaviour dataset for multi-view scenarios
author Soares da Costa, Tiago
author_facet Soares da Costa, Tiago
Andrade, Maria Teresa
Viana, Paula
Silva, Nuno Castro
author_role author
author2 Andrade, Maria Teresa
Viana, Paula
Silva, Nuno Castro
author2_role author
author
author
dc.contributor.none.fl_str_mv REPOSITÓRIO P.PORTO
dc.contributor.author.fl_str_mv Soares da Costa, Tiago
Andrade, Maria Teresa
Viana, Paula
Silva, Nuno Castro
dc.subject.por.fl_str_mv Multimedia; Multi-view; Head-tracking; Adaptive streaming; View prediction; Deep learning
topic Multimedia; Multi-view; Head-tracking; Adaptive streaming; View prediction; Deep learning
description The Data2MV dataset contains gaze fixation data obtained through experimental procedures from a total of 45 partic- ipants using an Intel RealSense F200 camera module and seven different video playlists. Each of the playlists had an approximate duration of 20 minutes and was viewed at least 17 times, with raw tracking data being recorded with a 0.05 second interval. The Data2MV dataset encompasses a total of 1.0 0 0.845 gaze fixations, gathered across a total of 128 exper- iments. It is also composed of 68.393 image frames, extracted from each of the 6 videos selected for these experiments, and an equal quantity of saliency maps, generated from aggregate fixation data. Software tools to obtain saliency maps and generate complementary plots are also provided as an open- source software package. The Data2MV dataset was publicly released to the research community on Mendeley Data and constitutes an important contribution to reduce the current scarcity of such data, particularly in immersive, multi-view streaming scenarios.
publishDate 2023
dc.date.none.fl_str_mv 2023-10-20
2023-10-20T00:00:00Z
2024-01-29T08:24:24Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/24730
url http://hdl.handle.net/10400.22/24730
dc.language.iso.fl_str_mv eng
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dc.relation.none.fl_str_mv 10.1016/j.dib.2023.109702
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dc.publisher.none.fl_str_mv Elsevier
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