Metraipolis › archive › dc_aa4e699883f1

Beyond Gait: Person Identification from Millimeter-Wave Point Clouds Across Activities of Daily Living — arXiv:2609.08818

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.08818
License (as archived): CC0-1.0
Archived: 2026-10-16T15:12:24.948Z
SHA-256 of the archived text: 3f3edea0f92da949e02546c11176dcd36d9e2f798d421c9ecf6aefff18c20fde
Stable URL of this page: https://metraipolis.com/documento/dc_aa4e699883f1

How to cite

Beyond Gait: Person Identification from Millimeter-Wave Point Clouds Across Activities of Daily Living — arXiv:2609.08818 — arXiv (en), https://arxiv.org/abs/2609.08818 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_aa4e699883f1 (Metraipolis archive dc_aa4e699883f1, sha256 3f3edea0f92da949e02546c11176dcd36d9e2f798d421c9ecf6aefff18c20fde)

BibTeX

@misc{dcaa4e699883f1,
  title = {Beyond Gait: Person Identification from Millimeter-Wave Point Clouds Across Activities of Daily Living — arXiv:2609.08818},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.08818},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_aa4e699883f1; sha256 3f3edea0f92da949e02546c11176dcd36d9e2f798d421c9ecf6aefff18c20fde},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.08818. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Person identification from millimeter-wave (mmWave) point clouds has mainly relied on gait. Indoor walking, however, is often brief and interrupted, while other activities of daily living (ADLs) may provide complementary identity information. We investigate identification across seven ADLs using mm-ADL, a new point-cloud dataset collected from 11 subjects under a controlled protocol. This extension introduces heterogeneous states and transitions whose spatial and temporal characteristics vary with activity. We therefore study whether activity can provide useful context for learning identity representations. We propose an activity-conditioned framework in which a human activity recognition router dispatches each clip to an activity-specific identity expert. The framework is implemented as a supervised mixture of experts, using a dual-stream static-dynamic PointNet (DS-SDPNet) to combine time-aggregated spatial structure with frame-to-frame information. We evaluate closed-set identification (ID) and subject-disjoint re-identification (ReID). With learned hard routing, ID accuracy increases from 62.1% to 68.0%. In a two-occupant ReID setting, hard routing increases mAP from 57.2% to 75.4% and Rank-1 accuracy from 59.1% to 82.1%. Under a matched gallery partition, activity-specific experts also outperform a shared embedding, showing that the gain extends beyond restricting the gallery. These results support the feasibility of using ADLs beyond gait for identification and the value of activity conditioning under controlled indoor conditions.

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).
Pagina del lavoro: https://arxiv.org/abs/2609.08818 — 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 2026-10-16T14:49:57.352Z 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).