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UAV-based intelligent traffic surveillance using recurrent neural networks and Swin transformer for dynamic
Mohammed Alshehri1, Ting Wu2, Nouf Abdullah Almujally3
1Department of Computer Science, King Khalid University, Abha, Saudi Arabia.
Introduction:
Urban traffic congestion, environmental degradation, and road safety challenges necessitate intelligent aerial robotic systems capable of real-time adaptive decision-making. Unmanned Aerial Vehicles (UAVs), with their flexible deployment and high vantage point, offer a promising solution for large-scale traffic surveillance in complex urban environments. This study introduces a UAV-based neural framework that addresses challenges such as asymmetric vehicle motion, scale variations, and spatial inconsistencies in aerial imagery.
Methods:
The proposed system integrates a multi-stage pipeline encompassing contrast enhancement and region-based clustering to optimize segmentation while maintaining computational efficiency for resource-constrained UAV platforms. Vehicle detection is carried out using a Recurrent Neural Network (RNN), optimized via a hybrid loss function combining cross-entropy and mean squared error to improve localization and confidence estimation. Upon detection, the system branches into two neural submodules: (i) a classification stream utilizing SURF and BRISK descriptors integrated with a Swin Transformer backbone for precise vehicle categorization, and (ii) a multi-object tracking stream employing DeepSORT, which fuses motion and appearance features within an affinity matrix for robust trajectory association.
Results:
Comprehensive evaluation on three benchmark UAV datasets-AU-AIR, UAVDT, and VAID shows consistent and high performance. The model achieved detection precisions of 0.913, 0.930, and 0.920; tracking precisions of 0.901, 0.881, and 0.890; and classification accuracies of 92.14, 92.75, and 91.25%, respectively.
Discussion:
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.
