SwinTrack: A Simple and Strong Baseline for Transformer Tracking (2112.00995v3)

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.

• The paper introduces a fully Transformer-based tracking framework that leverages Swin Transformer for both feature extraction and fusion.
• It integrates a lightweight motion token for incorporating temporal context, achieving 0.713 SUC on LaSOT and 96 fps in experiments.
• The approach challenges traditional CNN methods, offering a simple yet strong baseline that can inspire future research in visual tracking.

Analysis of SwinTrack: A Novel Transformer-Based Tracker

The development of Transformer architectures has presented new prospects for visual tracking, a domain traditionally dominated by Convolutional Neural Networks (CNNs). The paper "SwinTrack: A Simple and Strong Baseline for Transformer Tracking" introduces an innovative approach to visual tracking by proposing SwinTrack, a fully attentional tracker built within a classic Siamese framework. SwinTrack utilizes the Swin Transformer framework for feature representation and fusion, marking a deviation from the hybrid CNN-Transformer systems usually employed in state-of-the-art (SOTA) tracking methods.

Core Contributions and Methodology

The primary contribution of SwinTrack lies in its fully Transformer-based architecture where both representation learning and feature fusion are executed using Transformer modules. The Swin Transformer, known for its prowess in representation learning due to its attention mechanism, forms the backbone of SwinTrack. By leveraging the attention mechanism, SwinTrack achieves efficient feature interactions between the template and search regions, a feature that distinguishes it from conventional CNN and CNN-Transformer hybrid trackers.

SwinTrack introduces a novel "motion token" to enhance robustness in tracking tasks. This motion token encodes historical target trajectory within a local temporal window, thereby incorporating temporal context into the tracking framework. The motion token, being computationally lightweight, provides significant performance gains without burdening the computational overhead.

Performance and Experimental Insights

Extensive experiments validate SwinTrack's efficacy across multiple benchmarks, including the challenging LaSOT, where it establishes a new SUC (Success) record. SwinTrack outperforms existing approaches in terms of both accuracy and efficiency. The core model SwinTrack-B-384 achieves a prominent 0.713 SUC score on LaSOT, while the lighter SwinTrack-T-224 variant reaches a notable 0.672 SUC score, processing at 96 fps, making it competitive against existing SOTA methods in both accuracy and speed. This performance underscores the potential of adopting a Transformer-centric design for enhancing tracking robustness and precision.

Implications for Future Research

The implications of SwinTrack's architecture are substantial for future AI developments, particularly in visual tracking. By showcasing the advantages of a fully Transformer-based model, SwinTrack challenges the hegemony of CNNs in this domain, proposing an efficient alternative that can handle complex tracking scenarios with fewer assumptions on data spatial structure.

The introduction of the motion token also opens dialogue for incorporating richer temporal contexts in stateless modules, merging the strengths of sequence modelling inherent to Transformers with the robustness of Spatio-temporal features. This aligns with broader AI trends where temporal and spatial dynamics are key for enhancing model accuracy in dynamic environments.

Conclusion

SwinTrack’s fully attentional framework is not just an incremental innovation but holds a significant promise as a foundational model in the advancement of visual tracking. Its novel use of Transformers for both feature extraction and fusion, combined with the lightweight motion token, sets a precedent for future explorations in tracking architectures. While further exploration and refinement could elevate its applicability across broader tracking scenarios, SwinTrack undoubtedly contributes a robust baseline that can inspire future research directions within Transformer-based tracking systems.