Transformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking

Understand

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.