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Analysis and Synthesis of Traffic Scenes from Road Image Sequences

Traffic scene construction and simulation has been a hot topic in the community of intelligent transportation systems. In this paper, we propose a novel framework for the analysis and synthesis of traffic elements from road image sequences. The proposed framework is composed of three stages: traffic...

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Detalles Bibliográficos
Autores principales: Yuan, Sheng, Chen, Yuting, Huo, Huihui, Zhu, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730193/
https://www.ncbi.nlm.nih.gov/pubmed/33291772
http://dx.doi.org/10.3390/s20236939
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author Yuan, Sheng
Chen, Yuting
Huo, Huihui
Zhu, Li
author_facet Yuan, Sheng
Chen, Yuting
Huo, Huihui
Zhu, Li
author_sort Yuan, Sheng
collection PubMed
description Traffic scene construction and simulation has been a hot topic in the community of intelligent transportation systems. In this paper, we propose a novel framework for the analysis and synthesis of traffic elements from road image sequences. The proposed framework is composed of three stages: traffic elements detection, road scene inpainting, and road scene reconstruction. First, a new bidirectional single shot multi-box detector (BiSSD) method is designed with a global context attention mechanism for traffic elements detection. After the detection of traffic elements, an unsupervised CycleGAN is applied to inpaint the occlusion regions with optical flow. The high-quality inpainting images are then obtained by the proposed image inpainting algorithm. Finally, a traffic scene simulation method is developed by integrating the foreground and background elements of traffic scenes. The extensive experiments and comparisons demonstrate the effectiveness of the proposed framework.
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spelling pubmed-77301932020-12-12 Analysis and Synthesis of Traffic Scenes from Road Image Sequences Yuan, Sheng Chen, Yuting Huo, Huihui Zhu, Li Sensors (Basel) Article Traffic scene construction and simulation has been a hot topic in the community of intelligent transportation systems. In this paper, we propose a novel framework for the analysis and synthesis of traffic elements from road image sequences. The proposed framework is composed of three stages: traffic elements detection, road scene inpainting, and road scene reconstruction. First, a new bidirectional single shot multi-box detector (BiSSD) method is designed with a global context attention mechanism for traffic elements detection. After the detection of traffic elements, an unsupervised CycleGAN is applied to inpaint the occlusion regions with optical flow. The high-quality inpainting images are then obtained by the proposed image inpainting algorithm. Finally, a traffic scene simulation method is developed by integrating the foreground and background elements of traffic scenes. The extensive experiments and comparisons demonstrate the effectiveness of the proposed framework. MDPI 2020-12-04 /pmc/articles/PMC7730193/ /pubmed/33291772 http://dx.doi.org/10.3390/s20236939 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yuan, Sheng
Chen, Yuting
Huo, Huihui
Zhu, Li
Analysis and Synthesis of Traffic Scenes from Road Image Sequences
title Analysis and Synthesis of Traffic Scenes from Road Image Sequences
title_full Analysis and Synthesis of Traffic Scenes from Road Image Sequences
title_fullStr Analysis and Synthesis of Traffic Scenes from Road Image Sequences
title_full_unstemmed Analysis and Synthesis of Traffic Scenes from Road Image Sequences
title_short Analysis and Synthesis of Traffic Scenes from Road Image Sequences
title_sort analysis and synthesis of traffic scenes from road image sequences
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730193/
https://www.ncbi.nlm.nih.gov/pubmed/33291772
http://dx.doi.org/10.3390/s20236939
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