Effective and Robust: A Discriminative Temporal Learning Transformer for Satellite Videos

Le résumé fourni par la source

Robust feature learning has always been a research hotspot in dynamic temporal tasks. It makes the model almost unaffected by some challenging properties. The sequential nature of the transformer means attractive for temporal learning tasks, making it perform well in the video field. It is a current research hotspot for learning effective features by utilizing the target motion trends in satellite videos with multiple attributes, such as similar objects (SOBs) interference and occlusion. In this article, a novel discriminative temporal learning transformer tracker (DTLTracker) is introduced to characterize the dynamic target information for satellite videos. A discriminative transformer (DT) is proposed to comprehensively explore the dynamic target features with multiple attention mechanisms. It focuses on the primary information of the search area, making the target more discriminative. A fast convergence (FC) filter is designed to accelerate the weights convergence in calculating the target correlation operation, thereby ensuring the efficiency of model learning. The effectiveness and convergence have been demonstrated for the proposed optimization method. Additionally, a motion prior correction (MPC) module is constructed to utilize temporal information for target tracklet prediction, assisting the tracker in predicting the correct target. Numerous experiments are performed on three satellite videos to verify the effectiveness and feasibility of the proposed DTLTracker. It shows robustness compared to the state-of-the-art trackers on some challenging properties.

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