RaTrack: Moving Object Detection and Tracking with 4D Radar Point Cloud
This work addresses robust tracking for mobile autonomy applications like obstacle avoidance, but it is incremental as it adapts existing MOT concepts to a less-explored sensor modality.
The paper tackles moving object detection and tracking using 4D radar point clouds, introducing RaTrack to address radar noise and sparsity, and it achieves superior tracking precision on the View-of-Delft dataset, surpassing state-of-the-art methods.
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.