Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking

TL;DR

This paper introduces a transformer-based framework that leverages temporal context across video frames to significantly improve the robustness and accuracy of visual object tracking, outperforming existing methods.

Contribution

It designs a novel transformer architecture with separate encoder and decoder branches within a Siamese tracking pipeline, enhancing feature reinforcement and cue propagation for better tracking.

Findings

Outperforms current top trackers on benchmark datasets.

Sets new state-of-the-art records in visual tracking.

End-to-end trainable framework with transformer integration.

Abstract

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.