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In video object tracking, there exist rich temporal contexts among successiveframes, which have been largely overlooked in existing trackers. In this work,we bridge the individual video frames and explore the temporal contexts acrossthem via a transformer architecture for robust object tracking. Different fromclassic usage of the transformer in natural language processing tasks, weseparate its encoder and decoder into two parallel branches and carefullydesign them within the Siamese-like tracking pipelines. The transformer encoderpromotes the target templates via attention-based feature reinforcement, whichbenefits the high-quality tracking model generation. The transformer decoderpropagates the tracking cues from previous templates to the current frame,which facilitates the object searching process. Our transformer-assistedtracking framework is neat and trained in an end-to-end manner. With theproposed transformer, a simple Siamese matching approach is able to outperformthe current top-performing trackers. By combining our transformer with therecent discriminative tracking pipeline, our method sets several newstate-of-the-art records on prevalent tracking benchmarks.

Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking