A Unified Framework for Vehicle Detection, Tracking, and Counting Across Ground and Aerial Views Using Knowledge Distillation with YOLOv10-S.
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| Title: | A Unified Framework for Vehicle Detection, Tracking, and Counting Across Ground and Aerial Views Using Knowledge Distillation with YOLOv10-S. |
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| Authors: | Khan, Md Rezaul Karim1 (AUTHOR), Rishe, Naphtali1 (AUTHOR) rishen@cs.fiu.edu |
| Source: | Remote Sensing. Mar2026, Vol. 18 Issue 5, p842. 27p. |
| Subjects: | Traffic monitoring, Multiple target tracking, Aerial surveillance, Intelligent transportation systems, Object recognition (Computer vision), Vehicle detectors |
| Abstract: | Highlights: What are the main findings? The proposed unified and modular framework effectively integrates vehicle detection, multi-object tracking, and trajectory-based counting into a consistent end-to-end pipeline that works across both ground and aerial surveillance scenarios. Knowledge distillation strengthens the lightweight YOLOv10-S detector without architectural modification, improving temporal stability and enhancing overall system reliability across diverse viewpoints. What are the implications of the main findings? The study underscores the importance of evaluating traffic monitoring from a system-level perspective, where coordinated detection and tracking directly influence counting accuracy and robustness. The proposed framework provides a practical and scalable foundation for intelligent transportation applications, offering cross-domain adaptability and real-time feasibility for real-world traffic monitoring. Accurate and reliable vehicle detection, tracking, and counting across different surveillance platforms are fundamental requirements for developing smart Traffic Management Systems (TMS) and promoting sustainable urban mobility. Recent advances in both ground-level surveillance and remote sensing using deep learning have opened new opportunities for extracting detailed vehicular information from high-resolution aerial and surveillance video data. Our research reported here aims to present a unified, real-time vehicle analysis framework that integrates lightweight deep learning–based detection, robust multi-object tracking, and trajectory-driven counting within a single modular pipeline. The proposed framework employs a "You Only Look Once" system, YOLOv10-S as the detection backbone and enhances its robustness through supervision-level knowledge distillation without introducing any architectural modifications. Temporal consistency is enforced using an observation-centric multi-object tracking algorithm (OC-SORT), enabling stable identity preservation under camera motion and dense traffic conditions. Vehicle counting is performed using a trajectory-based virtual gate strategy, reducing duplicate counts and improving counting reliability. Comprehensive experiments conducted on the UA-DETRAC and VisDrone benchmarks show that the proposed framework effectively balances detection performance, tracking robustness, counting accuracy, and real-time efficiency in both ground-based and aerial surveillance settings. Furthermore, cross-dataset evaluations under direct train–test transfer highlight the inherent challenges of domain shift while showing that knowledge distillation consistently improves robustness in detection, tracking identity consistency, and vehicle counting. Overall, this framework enables effective real-world traffic monitoring by adopting a scalable and practical system design, where reliability is prioritized over architectural complexity. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192640099 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Unified Framework for Vehicle Detection, Tracking, and Counting Across Ground and Aerial Views Using Knowledge Distillation with YOLOv10-S. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Khan%2C+Md+Rezaul+Karim%22">Khan, Md Rezaul Karim</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rishe%2C+Naphtali%22">Rishe, Naphtali</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rishen@cs.fiu.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Mar2026, Vol. 18 Issue 5, p842. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Traffic+monitoring%22">Traffic monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Multiple+target+tracking%22">Multiple target tracking</searchLink><br /><searchLink fieldCode="DE" term="%22Aerial+surveillance%22">Aerial surveillance</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+transportation+systems%22">Intelligent transportation systems</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Vehicle+detectors%22">Vehicle detectors</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Highlights: What are the main findings? The proposed unified and modular framework effectively integrates vehicle detection, multi-object tracking, and trajectory-based counting into a consistent end-to-end pipeline that works across both ground and aerial surveillance scenarios. Knowledge distillation strengthens the lightweight YOLOv10-S detector without architectural modification, improving temporal stability and enhancing overall system reliability across diverse viewpoints. What are the implications of the main findings? The study underscores the importance of evaluating traffic monitoring from a system-level perspective, where coordinated detection and tracking directly influence counting accuracy and robustness. The proposed framework provides a practical and scalable foundation for intelligent transportation applications, offering cross-domain adaptability and real-time feasibility for real-world traffic monitoring. Accurate and reliable vehicle detection, tracking, and counting across different surveillance platforms are fundamental requirements for developing smart Traffic Management Systems (TMS) and promoting sustainable urban mobility. Recent advances in both ground-level surveillance and remote sensing using deep learning have opened new opportunities for extracting detailed vehicular information from high-resolution aerial and surveillance video data. Our research reported here aims to present a unified, real-time vehicle analysis framework that integrates lightweight deep learning–based detection, robust multi-object tracking, and trajectory-driven counting within a single modular pipeline. The proposed framework employs a "You Only Look Once" system, YOLOv10-S as the detection backbone and enhances its robustness through supervision-level knowledge distillation without introducing any architectural modifications. Temporal consistency is enforced using an observation-centric multi-object tracking algorithm (OC-SORT), enabling stable identity preservation under camera motion and dense traffic conditions. Vehicle counting is performed using a trajectory-based virtual gate strategy, reducing duplicate counts and improving counting reliability. Comprehensive experiments conducted on the UA-DETRAC and VisDrone benchmarks show that the proposed framework effectively balances detection performance, tracking robustness, counting accuracy, and real-time efficiency in both ground-based and aerial surveillance settings. Furthermore, cross-dataset evaluations under direct train–test transfer highlight the inherent challenges of domain shift while showing that knowledge distillation consistently improves robustness in detection, tracking identity consistency, and vehicle counting. Overall, this framework enables effective real-world traffic monitoring by adopting a scalable and practical system design, where reliability is prioritized over architectural complexity. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Remote Sensing is the property of MDPI 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: 10.3390/rs18050842 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 842 Subjects: – SubjectFull: Traffic monitoring Type: general – SubjectFull: Multiple target tracking Type: general – SubjectFull: Aerial surveillance Type: general – SubjectFull: Intelligent transportation systems Type: general – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Vehicle detectors Type: general Titles: – TitleFull: A Unified Framework for Vehicle Detection, Tracking, and Counting Across Ground and Aerial Views Using Knowledge Distillation with YOLOv10-S. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Khan, Md Rezaul Karim – PersonEntity: Name: NameFull: Rishe, Naphtali IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20724292 Numbering: – Type: volume Value: 18 – Type: issue Value: 5 Titles: – TitleFull: Remote Sensing Type: main |
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