  Vision-Language MOT is a crucial tracking problem and has drawn increasing
attention recently. It aims to track objects based on human language commands,
replacing the traditional use of templates or pre-set information from training
sets in conventional tracking tasks. Despite various efforts, a key challenge
lies in the lack of a clear understanding of why language is used for tracking,
which hinders further development in this field. In this paper, we address this
challenge by introducing Language-Guided MOT, a unified task framework, along
with a corresponding large-scale benchmark, termed LaMOT, which encompasses
diverse scenarios and language descriptions. Specially, LaMOT comprises 1,660
sequences from 4 different datasets and aims to unify various Vision-Language
MOT tasks while providing a standardized evaluation platform. To ensure
high-quality annotations, we manually assign appropriate descriptive texts to
each target in every video and conduct careful inspection and correction. To
the best of our knowledge, LaMOT is the first benchmark dedicated to
Language-Guided MOT. Additionally, we propose a simple yet effective tracker,
termed LaMOTer. By establishing a unified task framework, providing challenging
benchmarks, and offering insights for future algorithm design and evaluation,
we expect to contribute to the advancement of research in Vision-Language MOT.
We will release the data at https://github.com/Nathan-Li123/LaMOT.
