Although existing multi-object tracking (MOT) algorithms have obtained competitive performance on various benchmarks, almost all of them train and validate models on the same domain. The domain generalization problem of MOT is hardly studied. To bridge this gap, we first draw the observation that the high-level information contained in natural language is domain invariant to different tracking domains. Based on this observation, we propose to introduce natural language representation into visual MOT models for boosting the domain generalization ability. However, it is infeasible to label every tracking target with a textual description. To tackle this problem, we design two modules, namely visual context prompting (VCP) and visual-language mixing (VLM). Specifically, VCP generates visual prompts based on the input frames. VLM joints the information in the generated visual prompts and the textual prompts from a pre-defined Trackbook to obtain instance-level pseudo textual description, which is domain invariant to different tracking scenes. Through training models on MOT17 and validating them on MOT20, we observe that the pseudo textual descriptions generated by our proposed modules improve the generalization performance of query-based trackers by large margins.

通过引入自然语言表示来提高多目标跟踪模型的域泛化能力，本文提出了两个模块（视觉上下文提示和视觉-语言混合），用于生成本征于不同跟踪场景的实例级伪文字描述，并通过在 MOT17 上训练和在 MOT20 上验证，观察到该方法显著提高了基于查询的跟踪器的泛化性能。

引入自然语言表达式将多目标跟踪推广到未见过的领域