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Smart city article details
| Title | Smart Surveillance: Advanced Deep Learning-Based Vehicle Detection And Tracking Model On Uav Imagery |
|---|---|
| ID_Doc | 51478 |
| Authors | Othman K.M.; Alzaben N.; Alruwais N.; Maray M.; Darem A.A.; Mohamed A. |
| Year | 2025 |
| Published | Fractals, 33, 2 |
| DOI | http://dx.doi.org/10.1142/S0218348X25400274 |
| Abstract | Unmanned aerial vehicles (UAVs) can monitor traffic in different scenarios like surveillance, control, and security. The object detection method depends on UAVs equipped with vision sensors, which have received significant attention in domains such as intelligent transportation systems (ITSs) and UAVs, which can monitor road traffic across some distance and offer vital data for following intelligent traffic supervision tasks, namely traffic situational awareness, detecting sudden accidents, and calculating traffic flow. Nevertheless, most vehicle targets exhibit specific features and lesser sizes that challenge accurate vehicle recognition in UAV overhead view. Employing innovative computer vision (CV) models, vehicle recognition and tracking in UAV images contains detecting and following vehicles in aerial footage taken by UAVs. This procedure leverages deep learning (DL) approaches for perfectly detecting vehicles and a robust tracking method for monitoring their actions through the frames, offering vital information for traffic management, surveillance, and urban planning. Therefore, this study designs an Advanced DL-based Vehicle Detection and Tracking on UAV Imagery (ADLVDT-UAVI) approach. The drive of the ADLVDT-UAVI technique is to detect and classify distinct vehicles in the UAV images correctly as Brain-Like Computing technique for Traffic Flow Optimization in Smart Cities. In this approach, Gaussian filtering (GF) primarily eliminates the noise. Besides, the ADLVDT-UAVI technique utilizes a squeeze-and-excitation capsule network (SE-CapsNet) for feature vector derivation. Meanwhile, the hyperparameter selection process involves using the Fractals coati optimization algorithm (COA). Finally, the self-attention bidirectional long short-term memory (SA-BiLSTM) approach is utilized to classify detected vehicles. To validate the improved results of the ADLVDT-UAVI approach, a wide range of experiments is performed under VEDAI and ISPRS Postdam datasets. The experimental validation of the ADLVDT-UAVI approach portrayed the superior accuracy outcome of 98.35% and 98.96% compared to recent models. © The Author(s) |
| Author Keywords | Deep Learning; Fractals Coati Optimization Algorithm; Machine Learning; Smart Cities; UAV Imagery; Vehicle Detection |
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