A UAV Target Prediction and Tracking Method Based on KCF and Kalman Filter Hybrid Algorithm

When Unmanned Aerial Vehicle (UAV) follows a dynamic object as a target, it sometimes loses. In this paper, we propose a tracker switcher module to determine which algorithm is utilized for tracking. The author uses Kernelized Correlation Filter (KCF) to judge if the information from vision system is still reliable enough to leading UAV or lost. When the target is lost, we use Kalman Filter (KF) to predict the moving trajectory of the target, control the UAV movement and let UAV follow the virtual trajectory, until the target reappears and the system is in the correct tracking state again. Finally, we use OTB dataset to verify the feasibility of this method. Our tracker obtains Precision and FPS plot generator, which validate the effectiveness of this method. This method can make up for the problem under the premise of low delay, and has a wide range of application scenarios.