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arXiv Computer Vision By Mohamed Nagy, Naoufel Werghi, Bilal Hassan, Jorge Dias, Majid Khonji

Towards Accurate State Estimation: Motion Dynamics Kalman Filter for 3D Multi-Object Tracking

Read the original on arXiv Computer Vision →

The paper introduces a Motion Dynamics Kalman Filter (MD‑KF) for 3D multi‑object tracking that adapts to motion variation while keeping a single‑model structure. It improves state estimation by scaling motion components to reduce drift, especially for distant or occluded objects. Experiments on KITTI, Waymo Open Dataset, and real vehicle tests show MD‑KF matches multimodel KF performance with a 1.72× speedup.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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