UAV-based intelligent traffic surveillance using recurrent neural networks and Swin transformer for dynamic environments
These findings highlight the adaptability, robustness, and real-time viability of the proposed architecture in aerial traffic surveillance applications. By effectively integrating detection, classification, and tracking within a unified neural framework, the system contributes significant advancements to intelligent UAV-based traffic monitoring and supports future developments in smart city mobility and decision-making systems.
Authors
- Nouf Abdullah Almujally (ORCID: https://orcid.org/0000-0003-4405-9912)
- Hui Liu (ORCID: https://orcid.org/0000-0002-6850-9570)
- Mohammed S. Alshehri (ORCID: https://orcid.org/0000-0001-9471-7720)
- Muhammad Hanzla (ORCID: https://orcid.org/0009-0000-5494-8743)
- Ting Wu (ORCID: https://orcid.org/0000-0003-3660-2268)
- Yahya Alqahtani (ORCID: https://orcid.org/0000-0001-8622-0722)
- Ahmad Jalal (ORCID: https://orcid.org/0009-0000-8421-8477)
Institutions
- Princess Nourah bint Abdulrahman University (SA)
- University of Bremen (DE)
- Korea University (KR)
- Nanjing University of Science and Technology (CN)
- Tongren Hospital (CN)
- Guodian Institute of Science and Technology (CN)
- Southeast University (CN)
- King Khalid University (SA)
- Air University (PK)
Publication Details
- Journal
- Frontiers in Neurorobotics
- Published
- 2025-10-13
- DOI
- https://doi.org/10.3389/fnbot.2025.1681341
- Citations
- 11
- Primary Topic
- Video Surveillance and Tracking Methods
- Type
- article
- Field-Weighted Citation Impact
- 14.04
Funders
- Princess Nourah Bint Abdulrahman University
- King Khalid University