RT book part
T1 Adaptive Vehicle Detection in Urban Environments: A Self-learning Approach
A1 Guerrero Contreras, Gabriel José
A1 Balderas Díaz, Sara
A1 García Pascual, Abel
A1 Muñoz Ortega, Andrés
A2 Ingeniería Informática
K1 Self-Learning
K1 Semi-Supervised Learning
K1 Object Detection Models
K1 Adaptive Algorithms
K1 Ambient Intelligence
AB With increasing urbanization, efficient urban traffic management is a critical challenge that requires smarter and more adaptable systems. This paper introduces a self-learning algorithm designed to enhance the adaptability and effectiveness of vehicle detection models using urban camera infrastructures. By leveraging these ubiquitous devices, the study aims to capture and analyze real-time traffic data, a task traditionally limited by the need for extensive manual data labeling and the limitations of pre-trained models under varying urban conditions. Our self-learning algorithm addresses these challenges by reducing reliance on manual labeling and enabling continuous model adaptation to new conditions without direct human intervention. Implemented in the dynamic urban environment of the city of Madrid, Spain, this study evaluates the algorithm's capacity to enhance vehicle detection, considering a diverse range of vehicle types. The core of the algorithm comprises an iterative self-training process that refines model performance using both labeled and unlabeled data, thus progressively enhancing detection accuracy. Our findings reveal significant improvements in the ability of the model to accurately identify and classify vehicles, highlighting the potential of self-learning algorithms in urban traffic management.
PB Springer
SN 978-3-031-83116-4
YR 2025
FD 2025-02-27
LK http://hdl.handle.net/10498/37648
UL http://hdl.handle.net/10498/37648
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 05-nov-2026