@Article{cmc.2025.071804,
AUTHOR = {Mohammed Alnusayri, Ghulam Mujtaba, Nouf Abdullah Almujally, Shuoa S. Aitarbi, Asaad Algarni, Ahmad Jalal, Jeongmin Park},
TITLE = {Traffic Vision: UAV-Based Vehicle Detection and Traffic Pattern Analysis via Deep Learning Classifier},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {86},
YEAR = {2026},
NUMBER = {3},
PAGES = {--},
URL = {http://www.techscience.com/cmc/v86n3/65446},
ISSN = {1546-2226},
ABSTRACT = {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.},
DOI = {10.32604/cmc.2025.071804}
}