  3D Multi-object tracking (MOT) empowers mobile robots to accomplish
well-informed motion planning and navigation tasks by providing motion
trajectories of surrounding objects. However, existing 3D MOT methods typically
employ a single similarity metric and physical model to perform data
association and state estimation for all objects. With large-scale modern
datasets and real scenes, there are a variety of object categories that
commonly exhibit distinctive geometric properties and motion patterns. In this
way, such distinctions would enable various object categories to behave
differently under the same standard, resulting in erroneous matches between
trajectories and detections, and jeopardizing the reliability of downstream
tasks (navigation, etc.). Towards this end, we propose Poly-MOT, an efficient
3D MOT method based on the Tracking-By-Detection framework that enables the
tracker to choose the most appropriate tracking criteria for each object
category. Specifically, Poly-MOT leverages different motion models for various
object categories to characterize distinct types of motion accurately. We also
introduce the constraint of the rigid structure of objects into a specific
motion model to accurately describe the highly nonlinear motion of the object.
Additionally, we introduce a two-stage data association strategy to ensure that
objects can find the optimal similarity metric from three custom metrics for
their categories and reduce missing matches. On the NuScenes dataset, our
proposed method achieves state-of-the-art performance with 75.4\% AMOTA. The
code is available at https://github.com/lixiaoyu2000/Poly-MOT
