Open Access

ARTICLE

Traffic Vision: UAV-Based Vehicle Detection and Traffic Pattern Analysis via Deep Learning Classifier

Mohammed Alnusayri¹, Ghulam Mujtaba², Nouf Abdullah Almujally³, Shuoa S. Aitarbi⁴, Asaad Algarni⁵, Ahmad Jalal^2,6, Jeongmin Park^7,*

1 Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, 72388, Saudi Arabia
2 Faculty of Computing and AI, Air University, E-9, Islamabad, 44000, Pakistan
3 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
4 Department of Cyber Security, College of Humanities, Umm Al-Qura University, Makkah, 24382, Saudi Arabia
5 Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha, 91911, Saudi Arabia
6 Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul, 02841, Republic of Korea
7 Department of Computer Engineering, Tech University of Korea, 237 Sangidaehak-ro, Siheung-si, 15073, Gyeonggi-do, Republic of Korea

* Corresponding Author: Jeongmin Park. Email:

(This article belongs to the Special Issue: Advances in Object Detection and Recognition)

Computers, Materials & Continua 2026, 86(3), 7 https://doi.org/10.32604/cmc.2025.071804

Received 12 August 2025; Accepted 26 September 2025; Issue published 12 January 2026

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.

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Keywords

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