Intelligent traffic gesture recognition based on YOLOv8 algorithm optimization and OpenPose.
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| Title: | Intelligent traffic gesture recognition based on YOLOv8 algorithm optimization and OpenPose. |
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| Authors: | Zhang, J.1 zhj250407@163.com, Shi, H. O.1 |
| Source: | Advances in Transportation Studies. Jul2026, Vol. 69, p427-442. 16p. |
| Subjects: | Object recognition algorithms, Pose estimation (Computer vision), Optimization algorithms, Long short-term memory, Time series analysis, Image stabilization, Convolutional neural networks |
| Abstract: | To address the challenges of target loss due to extreme lighting interference, dynamic occlusion, and misjudgment of traffic gestures in complex traffic environments, this study proposes an intelligent traffic gesture recognition method that integrates an improved "You Only Look Once version 8" target detection algorithm with lightweight OpenPose pose estimation. First, an adaptive detection framework integrating physical optics preprocessing and optical flow motion compensation is constructed to achieve zero-latency target tracking. Second, a lightweight OpenPose based on a deep separable convolutional backbone network and a serial feature fusion mechanism is proposed for low-level skeleton extraction. Furthermore, a long short-term memory network and a dynamic time warping algorithm are dynamically introduced into the action classification decision layer for temporal feature interpolation and nonlinear optimal path alignment. Results show that in highly reflective scenes, the improved target detection algorithm achieves an average accuracy of 87.60% and a false detection rate of only 7.05%. The lightweight pose estimation algorithm achieves a correct keypoint ratio of 93.8% and an edge-end inference frame rate of up to 58 FPS. This demonstrates that the intelligent traffic gesture recognition method effectively overcomes physical visual interference and edge computing power bottlenecks in complex road environments, providing reliable technical support for intelligent vehicle decision-making in complex traffic scenarios. [ABSTRACT FROM AUTHOR] |
| Copyright of Advances in Transportation Studies is the property of Advances in Transportation Studies 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193950218 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Intelligent traffic gesture recognition based on YOLOv8 algorithm optimization and OpenPose. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhang%2C+J%2E%22">Zhang, J.</searchLink><relatesTo>1</relatesTo><i> zhj250407@163.com</i><br /><searchLink fieldCode="AR" term="%22Shi%2C+H%2E+O%2E%22">Shi, H. O.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Advances+in+Transportation+Studies%22">Advances in Transportation Studies</searchLink>. Jul2026, Vol. 69, p427-442. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Object+recognition+algorithms%22">Object recognition algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Pose+estimation+%28Computer+vision%29%22">Pose estimation (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Long+short-term+memory%22">Long short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Time+series+analysis%22">Time series analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Image+stabilization%22">Image stabilization</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To address the challenges of target loss due to extreme lighting interference, dynamic occlusion, and misjudgment of traffic gestures in complex traffic environments, this study proposes an intelligent traffic gesture recognition method that integrates an improved "You Only Look Once version 8" target detection algorithm with lightweight OpenPose pose estimation. First, an adaptive detection framework integrating physical optics preprocessing and optical flow motion compensation is constructed to achieve zero-latency target tracking. Second, a lightweight OpenPose based on a deep separable convolutional backbone network and a serial feature fusion mechanism is proposed for low-level skeleton extraction. Furthermore, a long short-term memory network and a dynamic time warping algorithm are dynamically introduced into the action classification decision layer for temporal feature interpolation and nonlinear optimal path alignment. Results show that in highly reflective scenes, the improved target detection algorithm achieves an average accuracy of 87.60% and a false detection rate of only 7.05%. The lightweight pose estimation algorithm achieves a correct keypoint ratio of 93.8% and an edge-end inference frame rate of up to 58 FPS. This demonstrates that the intelligent traffic gesture recognition method effectively overcomes physical visual interference and edge computing power bottlenecks in complex road environments, providing reliable technical support for intelligent vehicle decision-making in complex traffic scenarios. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Advances in Transportation Studies is the property of Advances in Transportation Studies 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: 10.53136/979122182735425 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 427 Subjects: – SubjectFull: Object recognition algorithms Type: general – SubjectFull: Pose estimation (Computer vision) Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Long short-term memory Type: general – SubjectFull: Time series analysis Type: general – SubjectFull: Image stabilization Type: general – SubjectFull: Convolutional neural networks Type: general Titles: – TitleFull: Intelligent traffic gesture recognition based on YOLOv8 algorithm optimization and OpenPose. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhang, J. – PersonEntity: Name: NameFull: Shi, H. O. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18245463 Numbering: – Type: volume Value: 69 Titles: – TitleFull: Advances in Transportation Studies Type: main |
| ResultId | 1 |
