AN OBJECT TRACKING METHOD BASED ON IMPROVED YOLOV3 MODEL AND KALMAN FILTER FOR UAV APPLICATIONS (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2024)
Unmanned Aerial Vehicles (UAVs) have gained significant attention in various applications, including surveillance, search and rescue, military tasks, delivery services, and object tracking. Efficient and accurate object tracking is a crucial task for enabling UAVs to perform complex missions autonomously. In this research paper, we propose an object tracking method that combines the improved YOLOv3 model and the Kalman filter to enhance the tracking capabilities of UAVs. The improved YOLOv3 model is utilized for real-time object detection, providing initial bounding box predictions. However, due to the inherent limitations of YOLOv3 in handling occlusions and abrupt motion changes, the proposed method incorporates a Kalman filter to refine and predict the object’s state over time. By fusing the object detection results with the Kalman filter, proposed method achieves robust and accurate tracking, even in challenging scenarios.
Keywords: object detection; improved YOLOv3 model; Kalman filter; unmanned aerial vehicle