Long-Term Motion-Assisted Remote Sensing Object Tracking

Remote sensing object tracking has gained significant attention due to its wide range of applications including surveillance and motion analysis. However, it faces various challenges such as low resolution, low contrast, blurring, and occlusion, which impede its development at a significantly slower pace compared to object tracking methods for general scenes. The challenges of low resolution, low contrast, and blurring result in weak target features, while the occlusion challenge poses a problem for target search range and tracker discrimination in subsequent frames. To address these issues, we propose a novel long-term motion-assisted framework, which can effectively mine long-term motion information and use an evaluation scheme for robust remote sensing object tracking. Specifically, we design a long-term motion feature mining module (LMFM), which efficiently calculates the long-term motion information by integrating previous motion features in a temporal-iterative manner to alleviate the problem of weak features caused by low resolution, low contrast, and blurring. Moreover, we design an evaluation scheme that combines the motion trajectory model, target classification scores, and predicted target positions to handle the issue of massive occlusion or target loss. Extensive experiments on the SatSOT, SV248S, and VISO datasets show that our approach outperforms state-of-the-art (SOTA) trackers. The source code, trained models, and raw results are released at https://github.com/zhaoxingle/LMANet.