OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception

A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Electronics (Basel) 2020-04, Vol.9 (4), p.560
Hauptverfasser: Mimouna, Amira, Alouani, Ihsen, Ben Khalifa, Anouar, El Hillali, Yassin, Taleb-Ahmed, Abdelmalik, Menhaj, Atika, Ouahabi, Abdeldjalil, Ben Amara, Najoua Essoukri
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue 4
container_start_page 560
container_title Electronics (Basel)
container_volume 9
creator Mimouna, Amira
Alouani, Ihsen
Ben Khalifa, Anouar
El Hillali, Yassin
Taleb-Ahmed, Abdelmalik
Menhaj, Atika
Ouahabi, Abdeldjalil
Ben Amara, Najoua Essoukri
description A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception. This is the first public, multimodal and synchronized dataset that includes UWB radar data, acoustic data, narrow-band radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, presenting four categories: pedestrian, cyclist, car and tram. The dataset includes various challenges related to dense urban traffic such as cluttered environment and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research.
doi_str_mv 10.3390/electronics9040560
format Article
fullrecord <record><control><sourceid>hal_cross</sourceid><recordid>TN_cdi_hal_primary_oai_HAL_hal_03140627v1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>oai_HAL_hal_03140627v1</sourcerecordid><originalsourceid>FETCH-LOGICAL-c325t-e508247667da89e592a1f0078264e289a7f475cd7de43c9db77572952997a0243</originalsourceid><addsrcrecordid>eNplkE9LAzEQxYMoWGq_gKdcPazO5s9m422ptS1saQ96XmIyqyvbTUnSgt_elooIvss8Hr-ZgUfIbQ73nGt4wB5tCn7obNQgQBZwQUYMlM400-zyj78mkxg_4Sid85LDiKzW9XK1eaQVXWDC4N9xQL-PdLXvU7f1zvT0ySQTMdHWB1q5gxksOjobDt3x5RaHRDcYLO5S54cbctWaPuLkZ47J6_PsZbrI6vV8Oa3qzHImU4YSSiZUUShnSo1SM5O3AKpkhUBWaqNaoaR1yqHgVrs3paRiWjKtlQEm-Jjcne9-mL7ZhW5rwlfjTdcsqro5ZcBzAQVTh_zIsjNrg48xYPu7kENz6q_53x__BlpNZKE</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Mimouna, Amira ; Alouani, Ihsen ; Ben Khalifa, Anouar ; El Hillali, Yassin ; Taleb-Ahmed, Abdelmalik ; Menhaj, Atika ; Ouahabi, Abdeldjalil ; Ben Amara, Najoua Essoukri</creator><creatorcontrib>Mimouna, Amira ; Alouani, Ihsen ; Ben Khalifa, Anouar ; El Hillali, Yassin ; Taleb-Ahmed, Abdelmalik ; Menhaj, Atika ; Ouahabi, Abdeldjalil ; Ben Amara, Najoua Essoukri</creatorcontrib><description>A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception. This is the first public, multimodal and synchronized dataset that includes UWB radar data, acoustic data, narrow-band radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, presenting four categories: pedestrian, cyclist, car and tram. The dataset includes various challenges related to dense urban traffic such as cluttered environment and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics9040560</identifier><language>eng</language><publisher>MDPI</publisher><subject>Artificial Intelligence ; Computer Science ; Electronics ; Engineering Sciences ; Networking and Internet Architecture ; Signal and Image processing</subject><ispartof>Electronics (Basel), 2020-04, Vol.9 (4), p.560</ispartof><rights>Attribution</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c325t-e508247667da89e592a1f0078264e289a7f475cd7de43c9db77572952997a0243</citedby><cites>FETCH-LOGICAL-c325t-e508247667da89e592a1f0078264e289a7f475cd7de43c9db77572952997a0243</cites><orcidid>0000-0001-5102-8087 ; 0000-0002-9946-0829 ; 0000-0001-7218-3799 ; 0000-0002-6392-7693 ; 0000-0002-6145-8475 ; 0000-0001-8750-1905 ; 0000-0003-3658-643X ; 0000-0002-3980-9902</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,27901,27902</link.rule.ids><backlink>$$Uhttps://hal.science/hal-03140627$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Mimouna, Amira</creatorcontrib><creatorcontrib>Alouani, Ihsen</creatorcontrib><creatorcontrib>Ben Khalifa, Anouar</creatorcontrib><creatorcontrib>El Hillali, Yassin</creatorcontrib><creatorcontrib>Taleb-Ahmed, Abdelmalik</creatorcontrib><creatorcontrib>Menhaj, Atika</creatorcontrib><creatorcontrib>Ouahabi, Abdeldjalil</creatorcontrib><creatorcontrib>Ben Amara, Najoua Essoukri</creatorcontrib><title>OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception</title><title>Electronics (Basel)</title><description>A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception. This is the first public, multimodal and synchronized dataset that includes UWB radar data, acoustic data, narrow-band radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, presenting four categories: pedestrian, cyclist, car and tram. The dataset includes various challenges related to dense urban traffic such as cluttered environment and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research.</description><subject>Artificial Intelligence</subject><subject>Computer Science</subject><subject>Electronics</subject><subject>Engineering Sciences</subject><subject>Networking and Internet Architecture</subject><subject>Signal and Image processing</subject><issn>2079-9292</issn><issn>2079-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNplkE9LAzEQxYMoWGq_gKdcPazO5s9m422ptS1saQ96XmIyqyvbTUnSgt_elooIvss8Hr-ZgUfIbQ73nGt4wB5tCn7obNQgQBZwQUYMlM400-zyj78mkxg_4Sid85LDiKzW9XK1eaQVXWDC4N9xQL-PdLXvU7f1zvT0ySQTMdHWB1q5gxksOjobDt3x5RaHRDcYLO5S54cbctWaPuLkZ47J6_PsZbrI6vV8Oa3qzHImU4YSSiZUUShnSo1SM5O3AKpkhUBWaqNaoaR1yqHgVrs3paRiWjKtlQEm-Jjcne9-mL7ZhW5rwlfjTdcsqro5ZcBzAQVTh_zIsjNrg48xYPu7kENz6q_53x__BlpNZKE</recordid><startdate>20200401</startdate><enddate>20200401</enddate><creator>Mimouna, Amira</creator><creator>Alouani, Ihsen</creator><creator>Ben Khalifa, Anouar</creator><creator>El Hillali, Yassin</creator><creator>Taleb-Ahmed, Abdelmalik</creator><creator>Menhaj, Atika</creator><creator>Ouahabi, Abdeldjalil</creator><creator>Ben Amara, Najoua Essoukri</creator><general>MDPI</general><scope>AAYXX</scope><scope>CITATION</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0001-5102-8087</orcidid><orcidid>https://orcid.org/0000-0002-9946-0829</orcidid><orcidid>https://orcid.org/0000-0001-7218-3799</orcidid><orcidid>https://orcid.org/0000-0002-6392-7693</orcidid><orcidid>https://orcid.org/0000-0002-6145-8475</orcidid><orcidid>https://orcid.org/0000-0001-8750-1905</orcidid><orcidid>https://orcid.org/0000-0003-3658-643X</orcidid><orcidid>https://orcid.org/0000-0002-3980-9902</orcidid></search><sort><creationdate>20200401</creationdate><title>OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception</title><author>Mimouna, Amira ; Alouani, Ihsen ; Ben Khalifa, Anouar ; El Hillali, Yassin ; Taleb-Ahmed, Abdelmalik ; Menhaj, Atika ; Ouahabi, Abdeldjalil ; Ben Amara, Najoua Essoukri</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c325t-e508247667da89e592a1f0078264e289a7f475cd7de43c9db77572952997a0243</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Artificial Intelligence</topic><topic>Computer Science</topic><topic>Electronics</topic><topic>Engineering Sciences</topic><topic>Networking and Internet Architecture</topic><topic>Signal and Image processing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mimouna, Amira</creatorcontrib><creatorcontrib>Alouani, Ihsen</creatorcontrib><creatorcontrib>Ben Khalifa, Anouar</creatorcontrib><creatorcontrib>El Hillali, Yassin</creatorcontrib><creatorcontrib>Taleb-Ahmed, Abdelmalik</creatorcontrib><creatorcontrib>Menhaj, Atika</creatorcontrib><creatorcontrib>Ouahabi, Abdeldjalil</creatorcontrib><creatorcontrib>Ben Amara, Najoua Essoukri</creatorcontrib><collection>CrossRef</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Electronics (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mimouna, Amira</au><au>Alouani, Ihsen</au><au>Ben Khalifa, Anouar</au><au>El Hillali, Yassin</au><au>Taleb-Ahmed, Abdelmalik</au><au>Menhaj, Atika</au><au>Ouahabi, Abdeldjalil</au><au>Ben Amara, Najoua Essoukri</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception</atitle><jtitle>Electronics (Basel)</jtitle><date>2020-04-01</date><risdate>2020</risdate><volume>9</volume><issue>4</issue><spage>560</spage><pages>560-</pages><issn>2079-9292</issn><eissn>2079-9292</eissn><abstract>A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception. This is the first public, multimodal and synchronized dataset that includes UWB radar data, acoustic data, narrow-band radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, presenting four categories: pedestrian, cyclist, car and tram. The dataset includes various challenges related to dense urban traffic such as cluttered environment and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research.</abstract><pub>MDPI</pub><doi>10.3390/electronics9040560</doi><orcidid>https://orcid.org/0000-0001-5102-8087</orcidid><orcidid>https://orcid.org/0000-0002-9946-0829</orcidid><orcidid>https://orcid.org/0000-0001-7218-3799</orcidid><orcidid>https://orcid.org/0000-0002-6392-7693</orcidid><orcidid>https://orcid.org/0000-0002-6145-8475</orcidid><orcidid>https://orcid.org/0000-0001-8750-1905</orcidid><orcidid>https://orcid.org/0000-0003-3658-643X</orcidid><orcidid>https://orcid.org/0000-0002-3980-9902</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2079-9292
ispartof Electronics (Basel), 2020-04, Vol.9 (4), p.560
issn 2079-9292
2079-9292
language eng
recordid cdi_hal_primary_oai_HAL_hal_03140627v1
source MDPI - Multidisciplinary Digital Publishing Institute; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
subjects Artificial Intelligence
Computer Science
Electronics
Engineering Sciences
Networking and Internet Architecture
Signal and Image processing
title OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T17%3A44%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-hal_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=OLIMP:%20A%20Heterogeneous%20Multimodal%20Dataset%20for%20Advanced%20Environment%20Perception&rft.jtitle=Electronics%20(Basel)&rft.au=Mimouna,%20Amira&rft.date=2020-04-01&rft.volume=9&rft.issue=4&rft.spage=560&rft.pages=560-&rft.issn=2079-9292&rft.eissn=2079-9292&rft_id=info:doi/10.3390/electronics9040560&rft_dat=%3Chal_cross%3Eoai_HAL_hal_03140627v1%3C/hal_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true