Highway Driving Dataset for Semantic Video Segmentation
Scene understanding is an essential technique in semantic segmentation. Although there exist several datasets that can be used for semantic segmentation, they are mainly focused on semantic image segmentation with large deep neural networks. Therefore, these networks are not useful for real time app...
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| creator | Kim, Byungju Yim, Junho Kim, Junmo |
| description | Scene understanding is an essential technique in semantic segmentation.
Although there exist several datasets that can be used for semantic
segmentation, they are mainly focused on semantic image segmentation with large
deep neural networks. Therefore, these networks are not useful for real time
applications, especially in autonomous driving systems. In order to solve this
problem, we make two contributions to semantic segmentation task. The first
contribution is that we introduce the semantic video dataset, the Highway
Driving dataset, which is a densely annotated benchmark for a semantic video
segmentation task. The Highway Driving dataset consists of 20 video sequences
having a 30Hz frame rate, and every frame is densely annotated. Secondly, we
propose a baseline algorithm that utilizes a temporal correlation. Together
with our attempt to analyze the temporal correlation, we expect the Highway
Driving dataset to encourage research on semantic video segmentation. |
| doi_str_mv | 10.48550/arxiv.2011.00674 |
| format | Article |
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Although there exist several datasets that can be used for semantic
segmentation, they are mainly focused on semantic image segmentation with large
deep neural networks. Therefore, these networks are not useful for real time
applications, especially in autonomous driving systems. In order to solve this
problem, we make two contributions to semantic segmentation task. The first
contribution is that we introduce the semantic video dataset, the Highway
Driving dataset, which is a densely annotated benchmark for a semantic video
segmentation task. The Highway Driving dataset consists of 20 video sequences
having a 30Hz frame rate, and every frame is densely annotated. Secondly, we
propose a baseline algorithm that utilizes a temporal correlation. Together
with our attempt to analyze the temporal correlation, we expect the Highway
Driving dataset to encourage research on semantic video segmentation.</description><identifier>DOI: 10.48550/arxiv.2011.00674</identifier><language>eng</language><subject>Computer Science - Computer Vision and Pattern Recognition</subject><creationdate>2020-11</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2011.00674$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2011.00674$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Kim, Byungju</creatorcontrib><creatorcontrib>Yim, Junho</creatorcontrib><creatorcontrib>Kim, Junmo</creatorcontrib><title>Highway Driving Dataset for Semantic Video Segmentation</title><description>Scene understanding is an essential technique in semantic segmentation.
Although there exist several datasets that can be used for semantic
segmentation, they are mainly focused on semantic image segmentation with large
deep neural networks. Therefore, these networks are not useful for real time
applications, especially in autonomous driving systems. In order to solve this
problem, we make two contributions to semantic segmentation task. The first
contribution is that we introduce the semantic video dataset, the Highway
Driving dataset, which is a densely annotated benchmark for a semantic video
segmentation task. The Highway Driving dataset consists of 20 video sequences
having a 30Hz frame rate, and every frame is densely annotated. Secondly, we
propose a baseline algorithm that utilizes a temporal correlation. Together
with our attempt to analyze the temporal correlation, we expect the Highway
Driving dataset to encourage research on semantic video segmentation.</description><subject>Computer Science - Computer Vision and Pattern Recognition</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj71OwzAURr0woJYHYMIvkNSOr-NkrNJCK1VioGKNbuzrYIkkyLUKfXv6N336lqNzGHuWIodKa7HA-BeOeSGkzIUoDTwyswn91y-e-CqGYxh7vsKEB0rcT5F_0IBjCpZ_BkfT-fYDjQlTmMY5e_D4faCn-87Y_nW9bzbZ7v1t2yx3GZ7xGRRSkiJ0VnpfQ627yjiFjrxVHemu8AIk1N6WSnVegXHW6qoAI8kJC07N2MsNezVvf2IYMJ7aS0F7LVD_ZMNBHw</recordid><startdate>20201101</startdate><enddate>20201101</enddate><creator>Kim, Byungju</creator><creator>Yim, Junho</creator><creator>Kim, Junmo</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20201101</creationdate><title>Highway Driving Dataset for Semantic Video Segmentation</title><author>Kim, Byungju ; Yim, Junho ; Kim, Junmo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a674-4211e3eadc1ff9495b87d3adefc3be5b2f04149fc633bf347dcc582471ed0c4d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Science - Computer Vision and Pattern Recognition</topic><toplevel>online_resources</toplevel><creatorcontrib>Kim, Byungju</creatorcontrib><creatorcontrib>Yim, Junho</creatorcontrib><creatorcontrib>Kim, Junmo</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kim, Byungju</au><au>Yim, Junho</au><au>Kim, Junmo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Highway Driving Dataset for Semantic Video Segmentation</atitle><date>2020-11-01</date><risdate>2020</risdate><abstract>Scene understanding is an essential technique in semantic segmentation.
Although there exist several datasets that can be used for semantic
segmentation, they are mainly focused on semantic image segmentation with large
deep neural networks. Therefore, these networks are not useful for real time
applications, especially in autonomous driving systems. In order to solve this
problem, we make two contributions to semantic segmentation task. The first
contribution is that we introduce the semantic video dataset, the Highway
Driving dataset, which is a densely annotated benchmark for a semantic video
segmentation task. The Highway Driving dataset consists of 20 video sequences
having a 30Hz frame rate, and every frame is densely annotated. Secondly, we
propose a baseline algorithm that utilizes a temporal correlation. Together
with our attempt to analyze the temporal correlation, we expect the Highway
Driving dataset to encourage research on semantic video segmentation.</abstract><doi>10.48550/arxiv.2011.00674</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Computer Vision and Pattern Recognition |
| title | Highway Driving Dataset for Semantic Video Segmentation |
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