Pan, Z;
Ding, F;
Zhong, H;
Lu, CX;
(2024)
RaTrack: Moving Object Detection and Tracking with 4D Radar Point Cloud.
In:
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) 2024.
(pp. pp. 4480-4487).
Institute of Electrical and Electronics Engineers (IEEE)
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Abstract
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.
Type: | Proceedings paper |
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Title: | RaTrack: Moving Object Detection and Tracking with 4D Radar Point Cloud |
Event: | 2024 IEEE International Conference on Robotics and Automation (ICRA) |
Location: | Yokohama, Japan |
Dates: | 13th-17th May 2024 |
ISBN-13: | 979-8-3503-8457-4 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/ICRA57147.2024.10610368 |
Publisher version: | http://dx.doi.org/10.1109/icra57147.2024.10610368 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10201175 |
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