TY  - EJOU
AU  - Alnusayri, Mohammed 
AU  - Mujtaba, Ghulam 
AU  - Almujally, Nouf Abdullah 
AU  - Aitarbi, Shuoa S. 
AU  - Algarni, Asaad 
AU  - Jalal, Ahmad 
AU  - Park, Jeongmin 

TI  - Traffic Vision: UAV-Based Vehicle Detection and Traffic Pattern Analysis via Deep Learning Classifier
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 86
IS  - 3
SN  - 1546-2226

AB  - This paper presents a unified Unmanned Aerial Vehicle-based (UAV-based) traffic monitoring framework that integrates vehicle detection, tracking, counting, motion prediction, and classification in a modular and co-optimized pipeline. Unlike prior works that address these tasks in isolation, our approach combines You Only Look Once (YOLO) v10 detection, ByteTrack tracking, optical-flow density estimation, Long Short-Term Memory-based (LSTM-based) trajectory forecasting, and hybrid Speeded-Up Robust Feature (SURF) + Gray-Level Co-occurrence Matrix (GLCM) feature engineering with VGG16 classification. Upon the validation across datasets (UAVDT and UAVID) our framework achieved a detection accuracy of 94.2%, and 92.3% detection accuracy when conducting a real-time UAV field validation. Our comprehensive evaluations, including multi-metric analyses, ablation studies, and cross-dataset validations, confirm the framework’s accuracy, efficiency, and generalizability. These results highlight the novelty of integrating complementary methods into a single framework, offering a practical solution for accurate and efficient UAV-based traffic monitoring.
KW  - Smart traffic system; drone devices; machine learner; dynamic complex scenes; VGG-16 classifier

DO  - 10.32604/cmc.2025.071804