LaMOT: Language-Guided Multi-Object Tracking

Vision-Language MOT is a critical tracking problem that has recently garnered increasing attention. It aims to track objects based on human language commands, displacing the traditional use of templates or pre-set information from training sets in conventional tracking tasks. However, a key challenge remains in understanding why language is used for tracking, hindering further development. In this paper, we introduce Language-Guided MOT, a unified task framework, and LaMOT, a corresponding large-scale benchmark, which encompasses diverse scenarios and language descriptions and comprises 1,660 sequences from 4 different datasets. The purpose of LaMOT is 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 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.