Efficient Road Traffic Video Congestion Classification Based on the Multi-Head Self-Attention Vision Transformer Model

Signals and images laboratory, Faculty of Electrical Engineering, Department of Electronics, University of Sciences and Technology of Oran Mohamed Boudiaf USTO-MB, Algeria

Asmâa Ouessai

asmaa.ouessai@univ-saida.dz

Faculty of Technology, Department of Telecommunications, Dr. Tahar Moulay University, Saida, Algeria

Nadir Kamel Benamara

nadirkamel.benamara@univ-usto.dz

Signals and images laboratory, Faculty of Electrical Engineering, Department of Electronics, University of Sciences and Technology of Oran Mohamed Boudiaf USTO-MB, Algeria

Mokhtar Keche

mokhtar.keche@univ-usto.dz

Signals and images laboratory, Faculty of Electrical Engineering, Department of Electronics, University of Sciences and Technology of Oran Mohamed Boudiaf USTO-MB, Algeria

Metrics

Articles in this issue

DOI: https://doi.org/10.2478/ttj-2024-0003 | Journal eISSN: 1407-6179 | Journal ISSN: 1407-6160

Journal RSS Feed

Language: English

Page range: 20 - 30

Published on: Feb 16, 2024

Published by: Transport and Telecommunication Institute

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

Road traffic classification,

macroscopic approach,

vision transformers,

multi-head self-attention,

deep learning

Related subjects:

Engineering,

Introductions and overviews,

Engineering, other

© 2024 Sofiane Abdelkrim Khalladi, Asmâa Ouessai, Nadir Kamel Benamara, Mokhtar Keche, published by Transport and Telecommunication Institute
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 25 (2024): Issue 1 (February 2024)