Optimizing road traffic surveillance: a robust hyper-heuristic approach for vehicle segmentation

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dc.contributor.authorRodríguez Esparza, Erick
dc.contributor.authorRamos Soto, Oscar
dc.contributor.authorMasegosa Arredondo, Antonio David
dc.contributor.authorOnieva Caracuel, Enrique
dc.contributor.authorOliva, Diego
dc.contributor.authorArriandiaga Laresgoiti, Ander
dc.contributor.authorGhosh, Arka
dc.date.accessioned2025-04-15T14:33:49Z
dc.date.available2025-04-15T14:33:49Z
dc.date.issued2024
dc.date.updated2025-04-15T14:33:49Z
dc.description.abstractDue to rising consumer demand and traffic congestion, last-mile logistics is becoming more challenging. To optimize urban distribution networks, digital image processing plays a key role in addressing these challenges through efficient traffic monitoring systems, an essential component of intelligent transportation systems. This paper introduces the Hyper-heuristic Genetic Algorithm based on Thompson Sampling with Diversity (HHGATSD), a novel approach to efficiently solving complex optimization and versatility problems in image segmentation. We evaluate its efficiency and robustness using the IEEE CEC2017 benchmark function set in general optimization problems with 30 and 50 dimensions. HHGATSD's applicability extends beyond optimization to computer vision in traffic management. First, the multilevel thresholding segmentation is performed on images extracted from the Berkeley Segmentation Dataset with minimum cross-entropy as the objective function, and its performance is compared using PSNR, SSIM, and FSIM metrics. Following that, the proposed methodology addresses the task of vehicle segmentation in traffic camera videos, reaffirming HHGATSD's effectiveness, adaptability, and consistency by consistently outperforming alternative segmentation methods found in the state-of-the-art. The results of comprehensive experiments, validated by statistical and non-parametric analyses, show that the proposed hyper-heuristic and methodology produce accurate and consistent segmentations for road traffic surveillance compared to the other methods in the literatureen
dc.description.sponsorshipThis work was supported in part by the University of Deusto Research Training Grants Programme through Spanish Ministry of Science and Innovation under Project PID2022-140612OB-I00; and in part by the Basque Government under Grant IT1564-22, Grant KK-2023/00012, and Grant KK-2023/00038en
dc.identifier.citationRodriguez-Esparza, E., Ramos-Soto, O., Masegosa, A. D., Onieva, E., Oliva, D., Arriandiaga, A., & Ghosh, A. (2024). Optimizing road traffic surveillance: a robust hyper-heuristic approach for vehicle segmentation. IEEE Access, 12, 29503-29524. https://doi.org/10.1109/ACCESS.2024.3369039
dc.identifier.doi10.1109/ACCESS.2024.3369039
dc.identifier.eissn2169-3536
dc.identifier.urihttp://hdl.handle.net/20.500.14454/2627
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights© 2024 The Authors
dc.subject.otherDigital image processing
dc.subject.otherHyper-heuristic optimization
dc.subject.otherIntelligent transportation systems
dc.subject.otherMultilevel thresholding
dc.subject.otherTraffic surveillance
dc.subject.otherVehicle segmentation
dc.titleOptimizing road traffic surveillance: a robust hyper-heuristic approach for vehicle segmentationen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage29524
oaire.citation.startPage29503
oaire.citation.titleIEEE Access
oaire.citation.volume12
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc-nd/4.0/
oaire.versionVoR
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