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Most deep trackers still follow the guidance of the siamese paradigms and usea template that contains only the target without any contextual information,which makes it difficult for the tracker to cope with large appearance changes,rapid target movement, and attraction from similar objects. To alleviate theabove problem, we propose a long-term context attention (LCA) module that canperform extensive information fusion on the target and its context fromlong-term frames, and calculate the target correlation while enhancing targetfeatures. The complete contextual information contains the location of thetarget as well as the state around the target. LCA uses the target state fromthe previous frame to exclude the interference of similar objects and complexbackgrounds, thus accurately locating the target and enabling the tracker toobtain higher robustness and regression accuracy. By embedding the LCA modulein Transformer, we build a powerful online tracker with a target-awarebackbone, termed as TATrack. In addition, we propose a dynamic online updatealgorithm based on the classification confidence of historical informationwithout additional calculation burden. Our tracker achieves state-of-the-artperformance on multiple benchmarks, with 71.1\% AUC, 89.3\% NP, and 73.0\% AOon LaSOT, TrackingNet, and GOT-10k. The code and trained models are availableon https://github.com/hekaijie123/TATrack.

Target-Aware Tracking with Long-term Context Attention