Optimizing Road Traffic Surveillance: A Robust Hyper-Heuristic Approach for Vehicle Segmentation
Due 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 intelligen...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10443601/ |
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| author | Erick Rodriguez-Esparza Oscar Ramos-Soto Antonio D. Masegosa Enrique Onieva Diego Oliva Ander Arriandiaga Arka Ghosh |
| author_facet | Erick Rodriguez-Esparza Oscar Ramos-Soto Antonio D. Masegosa Enrique Onieva Diego Oliva Ander Arriandiaga Arka Ghosh |
| author_sort | Erick Rodriguez-Esparza |
| collection | DOAJ |
| description | Due 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 literature. |
| first_indexed | 2024-03-07T19:11:20Z |
| format | Article |
| id | doaj.art-6f1a688043b74ef388a2973c9b72934e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-25T01:25:45Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6f1a688043b74ef388a2973c9b72934e2024-03-09T00:00:16ZengIEEEIEEE Access2169-35362024-01-0112295032952410.1109/ACCESS.2024.336903910443601Optimizing Road Traffic Surveillance: A Robust Hyper-Heuristic Approach for Vehicle SegmentationErick Rodriguez-Esparza0https://orcid.org/0000-0002-8761-1626Oscar Ramos-Soto1https://orcid.org/0000-0002-0598-8017Antonio D. Masegosa2https://orcid.org/0000-0001-7759-9072Enrique Onieva3https://orcid.org/0000-0001-9581-1823Diego Oliva4https://orcid.org/0000-0001-8781-7993Ander Arriandiaga5Arka Ghosh6DeustoTech, Faculty of Engineering, University of Deusto, Bilbao, SpainDepartamento de Ingeniería Electro-Fotónica, CUCEI, Universidad de Guadalajara, Guadalajara, Jalisco, MexicoDeustoTech, Faculty of Engineering, University of Deusto, Bilbao, SpainDeustoTech, Faculty of Engineering, University of Deusto, Bilbao, SpainDepartamento de Ingeniería Electro-Fotónica, CUCEI, Universidad de Guadalajara, Guadalajara, Jalisco, MexicoDeustoTech, Faculty of Engineering, University of Deusto, Bilbao, SpainDeustoTech, Faculty of Engineering, University of Deusto, Bilbao, SpainDue 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 literature.https://ieeexplore.ieee.org/document/10443601/Digital image processinghyper-heuristic optimizationintelligent transportation systemsmultilevel thresholdingtraffic surveillancevehicle segmentation |
| spellingShingle | Erick Rodriguez-Esparza Oscar Ramos-Soto Antonio D. Masegosa Enrique Onieva Diego Oliva Ander Arriandiaga Arka Ghosh Optimizing Road Traffic Surveillance: A Robust Hyper-Heuristic Approach for Vehicle Segmentation IEEE Access Digital image processing hyper-heuristic optimization intelligent transportation systems multilevel thresholding traffic surveillance vehicle segmentation |
| title | Optimizing Road Traffic Surveillance: A Robust Hyper-Heuristic Approach for Vehicle Segmentation |
| title_full | Optimizing Road Traffic Surveillance: A Robust Hyper-Heuristic Approach for Vehicle Segmentation |
| title_fullStr | Optimizing Road Traffic Surveillance: A Robust Hyper-Heuristic Approach for Vehicle Segmentation |
| title_full_unstemmed | Optimizing Road Traffic Surveillance: A Robust Hyper-Heuristic Approach for Vehicle Segmentation |
| title_short | Optimizing Road Traffic Surveillance: A Robust Hyper-Heuristic Approach for Vehicle Segmentation |
| title_sort | optimizing road traffic surveillance a robust hyper heuristic approach for vehicle segmentation |
| topic | Digital image processing hyper-heuristic optimization intelligent transportation systems multilevel thresholding traffic surveillance vehicle segmentation |
| url | https://ieeexplore.ieee.org/document/10443601/ |
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