FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2901291024> ?p ?o ?g. }

• W2901291024 endingPage "4981" @default.
• W2901291024 startingPage "4968" @default.
• W2901291024 abstract "This paper presents an advanced urban traffic density estimation solution using the latest deep learning techniques to intelligently process ultrahigh-resolution traffic videos taken from an unmanned aerial vehicle (UAV). We first capture nearly an hour-long ultrahigh-resolution traffic video at five busy road intersections of a modern megacity by flying a UAV during the rush hours. We then randomly sampled over 17 K 512×512 pixel image patches from the video frames and manually annotated over 64 K vehicles to form a dataset for this paper, which will also be made available to the research community for research purposes. Our innovative urban traffics analysis solution consists of an advanced deep neural network (DNN) based vehicle detection and localization, type (car, bus, and truck) recognition, tracking, and vehicle counting over time. We will present extensive experimental results to demonstrate the effectiveness of our solution. We will show that our enhanced single shot multibox detector (Enhanced-SSD) outperforms other DNN-based techniques and that deep learning techniques are more effective than traditional computer vision techniques in traffic video analysis. We will also show that ultrahigh-resolution video provides more information that enables more accurate vehicle detection and recognition than lower resolution contents. This paper not only demonstrates the advantages of using the latest technological advancements (ultrahigh-resolution video and UAV), but also provides an advanced DNN-based solution for exploiting these technological advancements for urban traffic density estimation." @default.
• W2901291024 created "2018-11-29" @default.
• W2901291024 creator A5027490342 @default.
• W2901291024 creator A5033865377 @default.
• W2901291024 creator A5035753061 @default.
• W2901291024 creator A5051616840 @default.
• W2901291024 creator A5066396136 @default.
• W2901291024 creator A5076092831 @default.
• W2901291024 creator A5083079327 @default.
• W2901291024 creator A5087635015 @default.
• W2901291024 date "2018-12-01" @default.
• W2901291024 modified "2023-10-05" @default.
• W2901291024 title "Urban Traffic Density Estimation Based on Ultrahigh-Resolution UAV Video and Deep Neural Network" @default.
• W2901291024 cites W1536680647 @default.
• W2901291024 cites W1594676769 @default.
• W2901291024 cites W1603572545 @default.
• W2901291024 cites W1903029394 @default.
• W2901291024 cites W1904073808 @default.
• W2901291024 cites W1910776219 @default.
• W2901291024 cites W1970892414 @default.
• W2901291024 cites W1972619302 @default.
• W2901291024 cites W2000841351 @default.
• W2901291024 cites W2017057754 @default.
• W2901291024 cites W2022515186 @default.
• W2901291024 cites W2029309869 @default.
• W2901291024 cites W2043552486 @default.
• W2901291024 cites W2066268896 @default.
• W2901291024 cites W2066916495 @default.
• W2901291024 cites W2068730032 @default.
• W2901291024 cites W2095537868 @default.
• W2901291024 cites W2105934661 @default.
• W2901291024 cites W2115739848 @default.
• W2901291024 cites W2119605622 @default.
• W2901291024 cites W2120432884 @default.
• W2901291024 cites W2124798378 @default.
• W2901291024 cites W2132744778 @default.
• W2901291024 cites W2133235827 @default.
• W2901291024 cites W2134759468 @default.
• W2901291024 cites W2141520041 @default.
• W2901291024 cites W2144506857 @default.
• W2901291024 cites W2145717442 @default.
• W2901291024 cites W2149469620 @default.
• W2901291024 cites W2150066425 @default.
• W2901291024 cites W2151103935 @default.
• W2901291024 cites W2155893237 @default.
• W2901291024 cites W2161969291 @default.
• W2901291024 cites W2168296665 @default.
• W2901291024 cites W2194775991 @default.
• W2901291024 cites W2252355370 @default.
• W2901291024 cites W2401150670 @default.
• W2901291024 cites W2463631526 @default.
• W2901291024 cites W2467917809 @default.
• W2901291024 cites W2483920485 @default.
• W2901291024 cites W2523253747 @default.
• W2901291024 cites W2550642934 @default.
• W2901291024 cites W2576886744 @default.
• W2901291024 cites W2618530766 @default.
• W2901291024 cites W2755424541 @default.
• W2901291024 cites W2792476967 @default.
• W2901291024 cites W2800655747 @default.
• W2901291024 cites W2963037989 @default.
• W2901291024 cites W3151739637 @default.
• W2901291024 cites W4248939017 @default.
• W2901291024 cites W639708223 @default.
• W2901291024 doi "https://doi.org/10.1109/jstars.2018.2879368" @default.
• W2901291024 hasPublicationYear "2018" @default.
• W2901291024 type Work @default.
• W2901291024 sameAs 2901291024 @default.
• W2901291024 citedByCount "60" @default.
• W2901291024 countsByYear W29012910242019 @default.
• W2901291024 countsByYear W29012910242020 @default.
• W2901291024 countsByYear W29012910242021 @default.
• W2901291024 countsByYear W29012910242022 @default.
• W2901291024 countsByYear W29012910242023 @default.
• W2901291024 crossrefType "journal-article" @default.
• W2901291024 hasAuthorship W2901291024A5027490342 @default.
• W2901291024 hasAuthorship W2901291024A5033865377 @default.
• W2901291024 hasAuthorship W2901291024A5035753061 @default.
• W2901291024 hasAuthorship W2901291024A5051616840 @default.
• W2901291024 hasAuthorship W2901291024A5066396136 @default.
• W2901291024 hasAuthorship W2901291024A5076092831 @default.
• W2901291024 hasAuthorship W2901291024A5083079327 @default.
• W2901291024 hasAuthorship W2901291024A5087635015 @default.
• W2901291024 hasBestOaLocation W29012910242 @default.
• W2901291024 hasConcept C108583219 @default.
• W2901291024 hasConcept C111919701 @default.
• W2901291024 hasConcept C154945302 @default.
• W2901291024 hasConcept C160633673 @default.
• W2901291024 hasConcept C205372480 @default.
• W2901291024 hasConcept C31972630 @default.
• W2901291024 hasConcept C41008148 @default.
• W2901291024 hasConcept C50644808 @default.
• W2901291024 hasConcept C76155785 @default.
• W2901291024 hasConcept C79403827 @default.
• W2901291024 hasConcept C94915269 @default.
• W2901291024 hasConcept C98045186 @default.
• W2901291024 hasConceptScore W2901291024C108583219 @default.
• W2901291024 hasConceptScore W2901291024C111919701 @default.

• next