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.

翻译：移动自主系统依赖于对动态环境的精确感知。在三维世界中稳健地跟踪移动目标，对于轨迹预测、障碍物规避和路径规划等应用起着关键作用。虽然当前大多数方法利用激光雷达或摄像头实现多目标跟踪，但4D成像雷达的能力在很大程度上仍未得到充分探索。针对4D雷达数据中的噪声和点稀疏性带来的挑战，我们提出了RaTrack，这是一种专为基于雷达的跟踪设计的创新解决方案。我们的方法绕过了通常对特定目标类型和三维边界框的依赖，而是聚焦于运动分割与聚类，并通过运动估计模块加以增强。在View-of-Delft数据集上的评估表明，RaTrack在移动目标的跟踪精度上表现卓越，大幅超越了当前最先进的性能。我们在 https://github.com/LJacksonPan/RaTrack 上公开发布了代码和模型。