Automated FHWA Vehicle Classification Using Combined Semantic and Geometric Features Extracted from Surveillance Videos.
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| Title: | Automated FHWA Vehicle Classification Using Combined Semantic and Geometric Features Extracted from Surveillance Videos. |
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| Authors: | Lu, Linjun1 (AUTHOR) ll718@cam.ac.uk, Dai, Fei2 (AUTHOR) fei.dai@mail.wvu.edu |
| Source: | Journal of Computing in Civil Engineering. May2025, Vol. 39 Issue 3, p1-16. 16p. |
| Subjects: | Intelligent transportation systems, Road construction, Video surveillance, Deep learning, Field research |
| Abstract: | The US Federal Highway Administration (FHWA) vehicle classification scheme is designed to serve multiple transportation needs, such as road infrastructure design, pavement maintenance scheduling, and traffic-induced emission estimation. Although a plethora of studies have advanced computer vision-based techniques for vehicle classification, no vision-based method has yet achieved the desired level of accuracy for all 13 FHWA vehicle category classifications primarily due to the interclass similarity issue, particularly among trucks. To fill this gap, this study developed a two-stage vision-based method that leverages both semantic and geometric features extracted from surveillance videos. In the first stage, a cascaded Mask R-CNN model is employed to classify vehicles into six broad categories based on semantic features (i.e., vehicle appearance). In the second stage, the geometric features (i.e., axle configuration) are extracted and exploited to further classify trucks into nine specific FHWA categories. Additionally, a verification scheme is introduced to validate the classification results with the aim of filtering out the misclassified vehicles and improving the overall classification accuracy. The proposed method was evaluated through field experiments under different traffic scenarios. It achieved an overall classification accuracy of 98.6% across all 13 FHWA vehicle categories, with an additional 1.1% improvement thanks to the verification scheme. This study contributes to the body of knowledge by introducing a more accurate method for FHWA vehicle classification, particularly for trucks, expanding the potential of image-based techniques in a variety of intelligent transportation applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Computing in Civil Engineering is the property of American Society of Civil Engineers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 183755292 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Automated FHWA Vehicle Classification Using Combined Semantic and Geometric Features Extracted from Surveillance Videos. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lu%2C+Linjun%22">Lu, Linjun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ll718@cam.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Dai%2C+Fei%22">Dai, Fei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> fei.dai@mail.wvu.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Computing+in+Civil+Engineering%22">Journal of Computing in Civil Engineering</searchLink>. May2025, Vol. 39 Issue 3, p1-16. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Intelligent+transportation+systems%22">Intelligent transportation systems</searchLink><br /><searchLink fieldCode="DE" term="%22Road+construction%22">Road construction</searchLink><br /><searchLink fieldCode="DE" term="%22Video+surveillance%22">Video surveillance</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Field+research%22">Field research</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The US Federal Highway Administration (FHWA) vehicle classification scheme is designed to serve multiple transportation needs, such as road infrastructure design, pavement maintenance scheduling, and traffic-induced emission estimation. Although a plethora of studies have advanced computer vision-based techniques for vehicle classification, no vision-based method has yet achieved the desired level of accuracy for all 13 FHWA vehicle category classifications primarily due to the interclass similarity issue, particularly among trucks. To fill this gap, this study developed a two-stage vision-based method that leverages both semantic and geometric features extracted from surveillance videos. In the first stage, a cascaded Mask R-CNN model is employed to classify vehicles into six broad categories based on semantic features (i.e., vehicle appearance). In the second stage, the geometric features (i.e., axle configuration) are extracted and exploited to further classify trucks into nine specific FHWA categories. Additionally, a verification scheme is introduced to validate the classification results with the aim of filtering out the misclassified vehicles and improving the overall classification accuracy. The proposed method was evaluated through field experiments under different traffic scenarios. It achieved an overall classification accuracy of 98.6% across all 13 FHWA vehicle categories, with an additional 1.1% improvement thanks to the verification scheme. This study contributes to the body of knowledge by introducing a more accurate method for FHWA vehicle classification, particularly for trucks, expanding the potential of image-based techniques in a variety of intelligent transportation applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Computing in Civil Engineering is the property of American Society of Civil Engineers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1061/JCCEE5.CPENG-6413 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1 Subjects: – SubjectFull: Intelligent transportation systems Type: general – SubjectFull: Road construction Type: general – SubjectFull: Video surveillance Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Field research Type: general Titles: – TitleFull: Automated FHWA Vehicle Classification Using Combined Semantic and Geometric Features Extracted from Surveillance Videos. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lu, Linjun – PersonEntity: Name: NameFull: Dai, Fei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 08873801 Numbering: – Type: volume Value: 39 – Type: issue Value: 3 Titles: – TitleFull: Journal of Computing in Civil Engineering Type: main |
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