Radar-Informed 3D Multi-Object Tracking under Adverse Conditions
The challenge of 3D multi-object tracking is achieving robustness in real-world applications, for example under adverse conditions and maintaining consistency as distance increases. To overcome these challenges, sensor fusion approaches that combine LiDAR, cameras, and radar have emerged. However, existing multimodal methods usually treat radar as another learned feature inside the network. When the overall model degrades in difficult environments, the robustness advantages that radar could provide are also reduced. In this paper we propose RadarMOT, a radar-informed 3D multi-object tracking framework that explicitly uses radar point clouds as additional observations to refine state estim...
| Authors | Bingxue Xu, Emil Hedemalm, Ajinkya Khoche et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 April 2026 |
| Last updated at source | 21 April 2026 |
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