High-resolution satellite video single object tracking based on thicksiam framework

Abstract

ABSTRACT High-resolution satellite videos realize the short-dated gaze observation of the designated area on the ground, and its emergence has improved the temporal resolution of remote sensing data to the second level. Single object tracking (SOT) task in satellite video has attracted considerable attention. However, it faces challenges such as complex background, poor object feature representation, and lack of publicly available datasets. To cope with these challenges, a ThickSiam framework consisting of a Thickened Residual Block Siamese Network (TRBS-Net) for extracting robust semantic features to obtain the initial tracking results and a Remoulded Kalman Filter (RKF) module for simultaneously correcting the trajectory and size of the targets is designed in this work. The results of TRBS-Net and RKF modules are combined by an N-frame-convergence mechanism to achieve accurate tracking results. Ablation experiments are implemented on our annotated dataset to evaluate the performance of the proposed ThickSiam framework and other 19 state-of-the-art trackers. The comparison results show that our ThickSiam tracker obtains a precision value of 0.991 and a success value of 0.755 while running at 56.849 FPS implemented on one NVIDIA GTX1070Ti GPU.