Remote Sensing Target Tracking for UAV Aerial Videos Based on Multi-Frequency Feature Enhancement

Remote sensing target tracking in UAV (Unmanned Aerial Vehicle) aerial videos has gradually become a research hotspot of UAV application areas, in this paper, we propose a remote sensing target tracking method for UAV aerial videos based on multi-frequency feature enhancement. The main contributions are as follows: first, We use SiamRPN as the basic tracking network to obtain the natural ability of multi-scale testing, which can adapt to the scale changing of remote sensing targets caused by changes in shooting perspective. Second, in the stage of feature extraction, we introduce a new multi-frequency feature representation method, which effectively improves feature expression ability of highly dynamic targets. Our algorithm is compared quantitatively and qualitatively with some state-of-the-art tracking algorithms by using a test dataset consisting of a UAV123 dataset and a homemade dataset, the results show that our algorithm improves the tracking accuracy while having strong timeliness.