UAV Target Tracking Algorithm Based on Kernel Correlation Filter

At present, there are still some problems in the tracking of ground targets by UAV, such as the poor tracking effect when the targets are moving rapidly, rotating and the target size is small. Therefore, this paper proposes an improved kernel correlation filtering algorithm. Firstly, the target is segmented adaptively according to the length-width ratio, and the maximum response is calculated by using the fusion feature, which is obtained by extracting the HOG feature and CN feature of each sub-block. Secondly, the position filter is used to locate the target, and the size filter estimates the size of the target. Finally, a re-detection mechanism is introduced to judge whether to update the filter based on the APCE value. The experimental results show that in the process of UAV target tracking, the improved algorithm can effectively reduce the influence of external interference on the tracking effect and improve the tracking effect.