L. A. Luna-Rodriguez
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
L. A. Luna-Rodriguez has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Armando García
Electronics and Electrical Engineering Department, Instititute technological of Sonora, Cd. Obregón, Sonora, Mexico, Mexico
1 shared publication - Erica Ruiz-Ibarra
Sonora Institute of Technology
1 shared publication - Fernando Hermosillo-Reynoso
Facultad de Ingenieria, Universidad Panamericana, Alvaro del Portillo 49, Zapopan 45010, Mexico
1 shared publication - Héctor Eduardo Gilardi Velázquez
Facultad de Ingeniería, Universidad Panamericana, Josemaría Escrivá de Balaguer 101, Aguascalientes 20290, Mexico
1 shared publication - Jose Alberto Del Puerto-Flores
Facultad de Ingenieria, Universidad Panamericana, Alvaro del Portillo 49, Zapopan 45010, Jalisco, Mexico
1 shared publication - Juan-Carlos López-Pimentel
Facultad de Ingenieria, Universidad Panamericana, Alvaro del Portillo 49, Zapopan 45010, Jalisco, Mexico
1 shared publication
Latest Publications
- Open Access
- 29 November 2025
- Published by MDPI AG in Mathematics
- Vol. 13 (23) , 3832
A Transformer-Based Multi-Task Learning Model for Vehicle Traffic Surveillance
Vehicle traffic surveillance (VTS) systems are based on the automatic analysis of video sequences to detect, classify, and track vehicles in urban environments. The design of new VTS systems requires computationally efficient architectures with high performance in accuracy. Conventional approaches based on multi-stage pipelines have been successfully used during the last decade. However, these systems need to be improved to face the challenges of complex, high-mobility traffic environments. This article proposes an efficient system based on transformer architectures for VTS channels. The proposed analysis system is evaluated in scenarios with high vehicle density and occlusions. The results demonstrate that the proposed scheme reduces the computational complexity required for multi-object detection and tracking and exhibits a Multiple Object Tracking Accuracy (MOTA) of 0.757 and an identity F1 score (IDF1) of 0.832 when compared to conventional multi-stage systems under the same conditions and parameters, along with achieving a high detection precision of 0.934. The results show the viability of implementing the proposed system in practical applications for high-density vehicle VTS channels.
- 29 November 2025
- Published by MDPI AG in Mathematics
- Vol. 13 (23) , 3832
Open AccessA Transformer-Based Multi-Task Learning Model for Vehicle Traffic Surveillance
Vehicle traffic surveillance (VTS) systems are based on the automatic analysis of video sequences to detect, classify, and track vehicles in urban environments. The design of new VTS systems requires computationally efficient architectures with high performance in accuracy. Conventional approaches based on multi-stage pipelines have been successfully used during the last decade. However, these systems need to be improved to face the challenges of complex, high-mobility traffic environments. This article proposes an efficient system based on transformer architectures for VTS channels. The proposed analysis system is evaluated in scenarios with high vehicle density and occlusions. The results demonstrate that the proposed scheme reduces the computational complexity required for multi-object detection and tracking and exhibits a Multiple Object Tracking Accuracy (MOTA) of 0.757 and an identity F1 score (IDF1) of 0.832 when compared to conventional multi-stage systems under the same conditions and parameters, along with achieving a high detection precision of 0.934. The results show the viability of implementing the proposed system in practical applications for high-density vehicle VTS channels.