SwinTrack: A Simple and Strong Baseline for Transformer Tracking

TL;DR

SwinTrack introduces a fully-attentional Transformer-based tracker within a Siamese framework, leveraging novel motion tokens for temporal context, achieving state-of-the-art results across multiple benchmarks.

Contribution

The paper presents a simple, efficient Transformer-based tracker with a novel motion token for temporal context, outperforming existing methods and serving as a strong baseline.

Findings

Achieves new SOTA on LaSOT with 0.713 SUC score

Outperforms existing approaches on multiple benchmarks

Introduces a lightweight motion token for improved robustness

Abstract

Recently Transformer has been largely explored in tracking and shown state-of-the-art (SOTA) performance. However, existing efforts mainly focus on fusing and enhancing features generated by convolutional neural networks (CNNs). The potential of Transformer in representation learning remains under-explored. In this paper, we aim to further unleash the power of Transformer by proposing a simple yet efficient fully-attentional tracker, dubbed SwinTrack, within classic Siamese framework. In particular, both representation learning and feature fusion in SwinTrack leverage the Transformer architecture, enabling better feature interactions for tracking than pure CNN or hybrid CNN-Transformer frameworks. Besides, to further enhance robustness, we present a novel motion token that embeds historical target trajectory to improve tracking by providing temporal context. Our motion token is lightweight with negligible computation but brings clear gains. In our thorough experiments, SwinTrack exceeds existing approaches on multiple benchmarks. Particularly, on the challenging LaSOT, SwinTrack sets a new record with 0.713 SUC score. It also achieves SOTA results on other benchmarks. We expect SwinTrack to serve as a solid baseline for Transformer tracking and facilitate future research. Our codes and results are released at https://github.com/LitingLin/SwinTrack.