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Segment Any Motion with Radar: Robust Multimodal Moving-Object Segmentation and Tracking — arXiv:2609.08346

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.08346
License (as archived): CC0-1.0
Archived: 2026-10-16T15:12:13.849Z
SHA-256 of the archived text: 59fb477621f04853f061faabd0d77a6f50e46dad6a164ca6dbf9ed40eb2b97e2
Stable URL of this page: https://metraipolis.com/documento/dc_7afca83809d8

How to cite

Segment Any Motion with Radar: Robust Multimodal Moving-Object Segmentation and Tracking — arXiv:2609.08346 — arXiv (en), https://arxiv.org/abs/2609.08346 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_7afca83809d8 (Metraipolis archive dc_7afca83809d8, sha256 59fb477621f04853f061faabd0d77a6f50e46dad6a164ca6dbf9ed40eb2b97e2)

BibTeX

@misc{dc7afca83809d8,
  title = {Segment Any Motion with Radar: Robust Multimodal Moving-Object Segmentation and Tracking — arXiv:2609.08346},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.08346},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_7afca83809d8; sha256 59fb477621f04853f061faabd0d77a6f50e46dad6a164ca6dbf9ed40eb2b97e2},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.08346. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Moving-object perception must decide which image regions correspond to real motion and keep every instance identified over time. Methods that read motion from appearance, optical flow, or estimated trajectories lose that evidence under poor illumination, adverse weather, reflections, and occlusion. Radar is a natural remedy because it measures radial velocity directly instead of inferring it from photometric correspondence. However, existing benchmarks do not jointly provide radar measurements, dense moving-instance masks, and temporally consistent identities for surveillance. We therefore introduce RGBTR-Motion, a synchronized and calibrated fixed-camera benchmark that pairs RGB, thermal, and radar streams with dense instance masks and temporally consistent identities across diverse surveillance scenes. We also develop SAM-Radar, an RGB, thermal, and radar-based segmentation and tracking framework built on SAM 3. SAM-Radar's radar-aware detector fuses calibrated RGBT features with radar returns that are grounded at their projected image locations, and motion supervision, implemented as foreground classification of those projected returns, teaches the detector to reject clutter without any text prompt. The tracker associates accepted radar returns with individual trajectories and uses them as physical evidence that a visually degraded target remains present. This allows it to bridge short periods of low visibility or occlusion and reconnect a reappearing target to its existing identity instead of starting a new track. SAM-Radar attains 0.7027 IoU and 0.8090 F1-50, and raises MOTA, HOTA, and IDF1 by 0.2977, 0.1603, and 0.2857 over the strongest competing values.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-09 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.08346 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T14:49:57.311Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).