一个模型搞定五类目标跟踪，省去重复训练。
SUTrack: Towards Simple and Unified Single Object Tracking
- 统一输入表示，用一个模型处理五种跟踪任务。
- 跨11个数据集表现超越专用模型，精度更高。
- 支持从边缘设备到高性能显卡的多种部署方案。
本文提出一种简单而统一的单目标跟踪（SOT）框架SUTrack，将五种SOT任务（基于RGB、RGB-深度、RGB-热成像、RGB-事件、RGB-语言）整合为一个在单次训练中完成的统一模型。由于数据特性差异，现有方法通常针对每类任务设计独立架构并分别训练，导致训练冗余、技术重复且跨模态知识难以共享。相比之下，SUTrack证明单一模型通过统一输入表示即可有效应对各类常见SOT任务，无需任务特定设计与分训。此外，我们引入任务识别辅助训练策略与软令牌类型嵌入，以极小开销进一步提升性能。实验表明，SUTrack在覆盖五类任务的11个数据集上均优于以往专用模型。同时，我们提供适配边缘设备与高性能GPU的多版本模型，在速度与精度间取得良好平衡。代码与模型已开源。
原文摘要 · Abstract (English)
In this paper, we propose a simple yet unified single object tracking (SOT) framework, dubbed SUTrack. It consolidates five SOT tasks (RGB-based, RGB-Depth, RGB-Thermal, RGB-Event, RGB-Language Tracking) into a unified model trained in a single session. Due to the distinct nature of the data, current methods typically design individual architectures and train separate models for each task. This fragmentation results in redundant training processes, repetitive technological innovations, and limited cross-modal knowledge sharing. In contrast, SUTrack demonstrates that a single model with a unified input representation can effectively handle various common SOT tasks, eliminating the need for task-specific designs and separate training sessions. Additionally, we introduce a task-recognition auxiliary training strategy and a soft token type embedding to further enhance SUTrack's performance with minimal overhead. Experiments show that SUTrack outperforms previous task-specific counterparts across 11 datasets spanning five SOT tasks. Moreover, we provide a range of models catering edge devices as well as high-performance GPUs, striking a good trade-off between speed and accuracy. We hope SUTrack could serve as a strong foundation for further compelling research into unified tracking models. Code and models are available at github.com/chenxin-dlut/SUTrack.
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