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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2024-01, Vol.12, p.1-1
Main Authors: Rodriguez-Esparza, Erick, Ramos-Soto, Oscar, Masegosa, Antonio D, Onieva, Enrique, Oliva, Diego, Arriandiaga, Ander, Ghosh, Arka
Format: Article
Language:English
Subjects:
Computer vision
Digital image processing
Digital images
Digital imaging
Entropy (Information theory)
Genetic algorithms
Heuristic
Heuristic algorithms
Heuristic methods
Hyper-heuristic optimization
Image analysis
Image processing
Image segmentation
Intelligent transportation systems
Logistics
Multilevel thresholding
Optimization
Roads
Surveillance
Traffic
Traffic congestion
Traffic control
Traffic management
Traffic surveillance
Vehicle dynamics
Vehicle Segmentation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3369039