Lane detection and spatiotemporal reconstruction using the macroblock predictions method
Detection and tracking of road lane markings offers several applications in intelligent transport systems (ITS). Although it is perceived as the simple task of isolating lanes on various types of roads, the accuracy of detection remains an issue. Several studies in recent literature have proposed so...
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oai:animorepository.dlsu.edu.ph:faculty_research-13692021-11-23T08:30:46Z Lane detection and spatiotemporal reconstruction using the macroblock predictions method Roxas, Edison A. Vicerra, Ryan Rhay P. Santos, Gil Nonato C. Dadios, Elmer P. Bandala, Argel A. Detection and tracking of road lane markings offers several applications in intelligent transport systems (ITS). Although it is perceived as the simple task of isolating lanes on various types of roads, the accuracy of detection remains an issue. Several studies in recent literature have proposed solutions to this problem; however, none of these have used the method of macroblock (MB) prediction. This paper focuses on the type of MB applied for lane detection, tracking, and predictions, as well as the trade-off between the accuracy and complexity of implementing the system. This study makes the following contributions: (1) best MB for spatiotemporal lane detection and reconstruction; (2) best function approximation for lane predictions; and (3) best MB in terms of performance under different conditions. © 2018 Fuji Technology Press.All Rights Reserved. 2018-09-01T07:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/370 https://animorepository.dlsu.edu.ph/context/faculty_research/article/1369/type/native/viewcontent Faculty Research Work Animo Repository Intelligent transportation systems Computer vision Lane lines (Roads) Automatic tracking Mechanical Engineering |
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Intelligent transportation systems Computer vision Lane lines (Roads) Automatic tracking Mechanical Engineering |
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Intelligent transportation systems Computer vision Lane lines (Roads) Automatic tracking Mechanical Engineering Roxas, Edison A. Vicerra, Ryan Rhay P. Santos, Gil Nonato C. Dadios, Elmer P. Bandala, Argel A. Lane detection and spatiotemporal reconstruction using the macroblock predictions method |
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Detection and tracking of road lane markings offers several applications in intelligent transport systems (ITS). Although it is perceived as the simple task of isolating lanes on various types of roads, the accuracy of detection remains an issue. Several studies in recent literature have proposed solutions to this problem; however, none of these have used the method of macroblock (MB) prediction. This paper focuses on the type of MB applied for lane detection, tracking, and predictions, as well as the trade-off between the accuracy and complexity of implementing the system. This study makes the following contributions: (1) best MB for spatiotemporal lane detection and reconstruction; (2) best function approximation for lane predictions; and (3) best MB in terms of performance under different conditions. © 2018 Fuji Technology Press.All Rights Reserved. |
format |
text |
author |
Roxas, Edison A. Vicerra, Ryan Rhay P. Santos, Gil Nonato C. Dadios, Elmer P. Bandala, Argel A. |
author_facet |
Roxas, Edison A. Vicerra, Ryan Rhay P. Santos, Gil Nonato C. Dadios, Elmer P. Bandala, Argel A. |
author_sort |
Roxas, Edison A. |
title |
Lane detection and spatiotemporal reconstruction using the macroblock predictions method |
title_short |
Lane detection and spatiotemporal reconstruction using the macroblock predictions method |
title_full |
Lane detection and spatiotemporal reconstruction using the macroblock predictions method |
title_fullStr |
Lane detection and spatiotemporal reconstruction using the macroblock predictions method |
title_full_unstemmed |
Lane detection and spatiotemporal reconstruction using the macroblock predictions method |
title_sort |
lane detection and spatiotemporal reconstruction using the macroblock predictions method |
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Animo Repository |
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2018 |
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https://animorepository.dlsu.edu.ph/faculty_research/370 https://animorepository.dlsu.edu.ph/context/faculty_research/article/1369/type/native/viewcontent |
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1718383397679661056 |