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

翻译：三维多目标跟踪通过提供周围物体的运动轨迹，使移动机器人能够完成精准的运动规划与导航任务。然而现有三维多目标跟踪方法通常对所有物体使用单一相似度度量与物理模型进行数据关联与状态估计。随着大规模现代数据集与真实场景的出现，各类物体普遍展现出独特的几何特性与运动模式，这种差异性导致不同类别物体在统一标准下表现各异，从而引发轨迹与检测结果的错误匹配，危及下游任务（如导航等）的可靠性。为此，我们提出Poly-MOT——一种基于检测跟踪框架的高效三维多目标跟踪方法，使跟踪器能够为每个目标类别选择最适配的跟踪准则。具体而言，Poly-MOT针对不同物体类别采用差异化运动模型以精确表征各类运动特征，同时将物体刚性结构约束引入特定运动模型，从而准确描述物体的高度非线性运动。此外，我们提出两阶段数据关联策略，确保物体能从三种自定义度量中选取其类别对应的最优相似度度量，并减少匹配遗漏。在NuScenes数据集上，本方法以75.4%的AMOTA指标达到当前最优性能。代码开源地址：https://github.com/lixiaoyu2000/Poly-MOT