A Transformer-Based Multi-Task Learning Model for Vehicle Traffic Surveillance
Journal
Mathematics
ISSN
2227-7390
Publisher
MDPI AG
Date Issued
2025-11-29
Author(s)
Fernando Hermosillo-Reynoso
Erica Ruiz-Ibarra
Armando García-Berumen
Vinoth Babu Kumaravelu
L. A. Luna-Rodriguez
Type
journal-article
Abstract
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.
License
Acceso Abierto.
URL License
How to cite
Hermosillo-Reynoso, F., López-Pimentel, J.-C., Ruiz-Ibarra, E., García-Berumen, A., Del-Puerto-Flores, J. A., Gilardi-Velazquez, H. E., Kumaravelu, V. B., & Luna-Rodriguez, L. A. (2025). A Transformer-Based Multi-Task Learning Model for Vehicle Traffic Surveillance. Mathematics, 13(23), 3832. https://doi.org/10.3390/math13233832
Table of contents
1. Introduction -- 2. Related Work -- 3. 3. Problem Statement and Mathematical Formulation -- 4. Transformer-Based Multi-Task Learning Model for Vehicle Traffic Surveillance -- 5. Experimental Results -- 6. Discussion -- 7. Conclusions.