A Two-Stage Method for Aerial Tracking in Adverse Weather Conditions

Abstract

To tackle the issue of aerial tracking failure in adverse weather conditions, we developed an innovative two-stage tracking method, which incorporates a lightweight image restoring model DADNet and an excellent pretrained tracker. Our method begins by restoring the degraded image, which yields a refined intermediate result. Then, the tracker capitalizes on this intermediate result to produce precise tracking bounding boxes. To expand the UAV123 dataset to various weather scenarios, we estimated the depth of the images in the dataset. Our method was tested on two famous trackers, and the experimental results highlighted the superiority of our method. The comparison experiment’s results also validated the dehazing effectiveness of our restoration model. Additionally, the components of our dehazing module were proven efficient through ablation studies.