  Mobile autonomy relies on the precise perception of dynamic environments.
Robustly tracking moving objects in 3D world thus plays a pivotal role for
applications like trajectory prediction, obstacle avoidance, and path planning.
While most current methods utilize LiDARs or cameras for Multiple Object
Tracking (MOT), the capabilities of 4D imaging radars remain largely
unexplored. Recognizing the challenges posed by radar noise and point sparsity
in 4D radar data, we introduce RaTrack, an innovative solution tailored for
radar-based tracking. Bypassing the typical reliance on specific object types
and 3D bounding boxes, our method focuses on motion segmentation and
clustering, enriched by a motion estimation module. Evaluated on the
View-of-Delft dataset, RaTrack showcases superior tracking precision of moving
objects, largely surpassing the performance of the state of the art. We release
our code and model at https://github.com/LJacksonPan/RaTrack.
