arXiv:2607. 13891v1 Announce Type: new Abstract: Multi-object detection and tracking from noisy point clouds remain challenging in many data-scarce radar applications.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
Multi-object detection and tracking from noisy point clouds remain challenging in many data-scarce radar applications. Current Bayesian trackers based on Poisson measurement models offer a training-free solution but struggle to achieve accuracy and efficiency under severe clutter, large object populations, and full-resolution Doppler point clouds.
Sparse and noisy millimeter-wave radar point cloud observations often correspond to multiple plausible human poses, making deterministic pose estimation fundamentally ill-posed. Yet existing radar methods remain deterministic, collapsing this ambiguity into a single estimate.
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