  Despite recent significant progress, Multi-Object Tracking (MOT) faces
limitations such as reliance on prior knowledge and predefined categories and
struggles with unseen objects. To address these issues, Generic Multiple Object
Tracking (GMOT) has emerged as an alternative approach, requiring less prior
information. However, current GMOT methods often rely on initial bounding boxes
and struggle to handle variations in factors such as viewpoint, lighting,
occlusion, and scale, among others. Our contributions commence with the
introduction of the \textit{Referring GMOT dataset} a collection of videos,
each accompanied by detailed textual descriptions of their attributes.
Subsequently, we propose $\mathtt{Z-GMOT}$, a cutting-edge tracking solution
capable of tracking objects from \textit{never-seen categories} without the
need of initial bounding boxes or predefined categories. Within our
$\mathtt{Z-GMOT}$ framework, we introduce two novel components: (i)
$\mathtt{iGLIP}$, an improved Grounded language-image pretraining, for
accurately detecting unseen objects with specific characteristics. (ii)
$\mathtt{MA-SORT}$, a novel object association approach that adeptly integrates
motion and appearance-based matching strategies to tackle the complex task of
tracking objects with high similarity. Our contributions are benchmarked
through extensive experiments conducted on the Referring GMOT dataset for GMOT
task. Additionally, to assess the generalizability of the proposed
$\mathtt{Z-GMOT}$, we conduct ablation studies on the DanceTrack and MOT20
datasets for the MOT task. Our dataset, code, and models are released at:
https://fsoft-aic.github.io/Z-GMOT.
