UAV Detection Using Template Matching and Centroid Tracking

• Published in: IEEE access Vol. 12; pp. 129362 - 129375
• Main Authors: Hanzla, Muhammad, Yusuf, Muhammad Ovais, Sadiq, Touseef, Mudawi, Naif Al, Rahman, Hameedur, Alazeb, Abdulwahab, AlArfaj, Aisha Ahmed, Algarni, Asaad
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Automatic vehicle identification systems; Autonomous aerial vehicles; Centroids; Computer vision; Damage detection; Disaster management; Drone aircraft; Emergency preparedness; Feature extraction; Georeferencing; Image processing; Image segmentation; Machine learning; Monitoring; multiple object detection; Real-time systems; Roads; Satellite imagery; Satellite tracking; segmentation; smart traffic monitoring; Smart transportation; Template matching; Traffic control; Traffic flow; Traffic management; Traffic planning; Unmanned aerial vehicles; Urban planning; Vehicle detection; Vehicles
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3450580

In computer vision and image processing, vehicle detection and tracking in complicated aerial images have become important subjects. The need for automated systems that can precisely detect and track vehicles in aerial image data is growing due to the abundance of data coming from numerous sources, including drones and satellites. This study introduces a new method for lane extraction that relies on centroid tracking and template matching, followed by co-registration and geo-referencing. Our approach offers robust vehicle detection and tracking over a range of sizes and positions in complicated backgrounds, while also efficiently segmenting the region of interest. Our suggested method, which makes use of machine learning and feature extraction techniques, shows excellent precision and effectiveness when it comes to detecting and tracking vehicles in complex aerial images. This finding has important implications for traffic management and urban planning, going beyond computer vision and image processing. Our technology has the potential to transform traffic management procedures by making it simpler to detect traffic bottlenecks and monitor traffic flow. Furthermore, our method can help identify damaged vehicles in disaster response scenarios, which will help prioritize rescue and recovery activities. All things considered, our suggested approach is a significant addition to the domains of computer vision and image processing, with a broad range of uses in traffic control, urban planning, and disaster management.