Muhammad Hanzla ¹ Muhammad O. Yusuf ¹ Naif Al Mudawi ² Touseef Sadiq ^3* Nouf A. Almujally ⁴ Hameedur Rahman ^1* Abdulwahab Alazeb ² Asaad Algarni ⁵

• ¹ Air University, Islamabad, Pakistan
• ² Najran University, Najran, Saudi Arabia
• ³ University of Agder, Kristiansand, Vest-Agder, Norway
• ⁴ Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
• ⁵ Northern Border University, Arar, Northern Borders, Saudi Arabia

Unmanned aerial vehicles (UAVs) are widely used in various computer vision applications, especially in intelligent traffic monitoring, as they are agile and simplify operations while boosting efficiency. However, automating these procedures is still a significant challenge due to the difficulty of extracting foreground (vehicle) information from complex traffic scenes. This paper presents a unique method for autonomous vehicle surveillance that uses FCM to segment aerial images. YOLOv8, which is known for its ability to detect tiny objects, is then used to detect vehicles. Additionally, a system that utilizes ORB features is employed to support vehicle recognition, assignment, and recovery across picture frames. Vehicle tracking is accomplished using DeepSORT, which elegantly combines Kalman filtering with deep learning to achieve precise results. Our proposed model demonstrates remarkable performance in vehicle identification and tracking with precision of 0.86 and 0.84 on the VEDAI and SRTID datasets, respectively, for vehicle detection. For vehicle tracking, the model achieves accuracies of 0.89 and 0.85 on the VEDAI and SRTID datasets, respectively.