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DC Field | Value | Language |
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dc.contributor.author | Wang, J | - |
dc.contributor.author | Chen, Y | - |
dc.contributor.author | Ji, X | - |
dc.contributor.author | Dong, Z | - |
dc.contributor.author | Gao, M | - |
dc.contributor.author | Lai, CS | - |
dc.date.accessioned | 2024-10-01T09:02:36Z | - |
dc.date.available | 2024-10-01T09:02:36Z | - |
dc.date.issued | 2024-05-22 | - |
dc.identifier | ORCiD: Junfan Wang https://orcid.org/0000-0001-8403-2875 | - |
dc.identifier | ORCiD: Xiaoyue Ji https://orcid.org/0000-0002-3526-5215 | - |
dc.identifier | ORCiD: Zhekang Dong https://orcid.org/0000-0003-4639-3834 | - |
dc.identifier | ORCiD: Mingyu Gao https://orcid.org/0000-0002-5930-9526 | - |
dc.identifier | ORCiD: Chun Sing Lai https://orcid.org/0000-0002-4169-4438 | - |
dc.identifier.citation | Wang, J. et al. (2024) 'Metaverse Meets Intelligent Transportation System: An Efficient and Instructional Visual Perception Framework', IEEE Transactions on Intelligent Transportation Systems, 0 (early access), pp. 1 - 16. doi: 10.1109/TITS.2024.3398586. | en_US |
dc.identifier.issn | 1524-9050 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/29856 | - |
dc.description.abstract | The combination of the Metaverse and intelligent transportation systems (ITS) holds significant developmental promise, especially for visual perception tasks. However, the acquisition of high-quality scene data poses a challenging and expensive endeavor. Meanwhile, the visual disparity between the Metaverse and the physical world poses an impact on the practical applicability of the visual perception tasks. In this paper, a Metaverse Intelligent Traffic Visual Framework, MITVF, is developed to guide the implementation of visual perception tasks in the physical world. Firstly, a two-stage metadata optimization strategy is proposed that can efficiently provide diverse and high-quality scene data for traffic perception models. Specifically, an element reconfigurability strategy is proposed to flexibly combine dynamic and static traffic elements to enrich the data with a low cost. A diffusion model-based metadata optimization acceleration strategy is proposed to achieve efficient improvement of image resolution. Secondly, a Meta-Physical adaptive learning method is proposed, and further applied to visual perception tasks to compensate for the visual disparity between the Metaverse and the physical world. Experimental results show that MITVF achieves a 10 × acceleration in optimization speed, ensuring the image quality and reconstructing diverse. Further, MITVF is applied to the traffic object detection task to verify the effectiveness and validity. The performance of the model trained with 5k real data exceeded that of the model trained with 200k real data, with AP 50 reaching 67.7%. | en_US |
dc.description.sponsorship | Key Research and Development Project of Hangzhou (Grant Number: 2022AIZD0009 and 2022AIZD0022); 10.13039/100022963-Key Research and Development Program of Zhejiang Province (Grant Number: 2022C01062). | en_US |
dc.format.extent | 1 - 16 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © 2024 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works (see: https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/). | - |
dc.rights.uri | https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | - |
dc.subject | adaptive learning method | en_US |
dc.subject | metadata optimization strategy | en_US |
dc.subject | MITVF | en_US |
dc.subject | metaverse | en_US |
dc.title | Metaverse Meets Intelligent Transportation System: An Efficient and Instructional Visual Perception Framework | en_US |
dc.type | Article | en_US |
dc.date.dateAccepted | 2024-05-05 | - |
dc.identifier.doi | https://doi.org/10.1109/TITS.2024.3398586 | - |
dc.relation.isPartOf | IEEE Transactions on Intelligent Transportation Systems | - |
pubs.issue | 00 | - |
pubs.publication-status | Published | - |
pubs.volume | 0 | - |
dc.identifier.eissn | 1558-0016 | - |
dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © 2024 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works (see: https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/). | 12.48 MB | Adobe PDF | View/Open |
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