  In video object tracking, there exist rich temporal contexts among successive
frames, which have been largely overlooked in existing trackers. In this work,
we bridge the individual video frames and explore the temporal contexts across
them via a transformer architecture for robust object tracking. Different from
classic usage of the transformer in natural language processing tasks, we
separate its encoder and decoder into two parallel branches and carefully
design them within the Siamese-like tracking pipelines. The transformer encoder
promotes the target templates via attention-based feature reinforcement, which
benefits the high-quality tracking model generation. The transformer decoder
propagates the tracking cues from previous templates to the current frame,
which facilitates the object searching process. Our transformer-assisted
tracking framework is neat and trained in an end-to-end manner. With the
proposed transformer, a simple Siamese matching approach is able to outperform
the current top-performing trackers. By combining our transformer with the
recent discriminative tracking pipeline, our method sets several new
state-of-the-art records on prevalent tracking benchmarks.
