Despite the significant progress made in recent years, Multi-Object Tracking
(MOT) approaches still suffer from several limitations, including their
reliance on prior knowledge of tracking targets, which necessitates the costly
annotation of large labeled datasets. As a result, existing MOT methods are
limited to a small set of predefined categories, and they struggle with unseen
objects in the real world. To address these issues, Generic Multiple Object
Tracking (GMOT) has been proposed, which requires less prior information about
the targets. However, all existing GMOT approaches follow a one-shot paradigm,
relying mainly on the initial bounding box and thus struggling to handle
variants e.g., viewpoint, lighting, occlusion, scale, and etc. In this paper,
we introduce a novel approach to address the limitations of existing MOT and
GMOT methods. Specifically, we propose a zero-shot GMOT (Z-GMOT) algorithm that
can track never-seen object categories with zero training examples, without the
need for predefined categories or an initial bounding box. To achieve this, we
propose iGLIP, an improved version of Grounded language-image pretraining
(GLIP), which can detect unseen objects while minimizing false positives. We
evaluate our Z-GMOT thoroughly on the GMOT-40 dataset, AnimalTrack testset,
DanceTrack testset. The results of these evaluations demonstrate a significant
improvement over existing methods. For instance, on the GMOT-40 dataset, the
Z-GMOT outperforms one-shot GMOT with OC-SORT by 27.79 points HOTA and 44.37
points MOTA. On the AnimalTrack dataset, it surpasses fully-supervised methods
with DeepSORT by 12.55 points HOTA and 8.97 points MOTA. To facilitate further
research, we will make our code and models publicly available upon acceptance
of this paper.

我们介绍了一种新方法，称为零样本通用多目标跟踪算法（Z-GMOT），它可以跟踪从未见过的物体类别，而无需预定义类别或初始边界框。我们使用 iGLIP 对 Z-GMOT 进行评估，结果表明在多个数据集上的表现优于现有方法。