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Training-Free Hold-Usage Detection in Sport Climbing with Foundation Pose Models — arXiv:2609.30026

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.30026
License (as archived): CC0-1.0
Archived: 2026-10-08T06:44:41.612Z
SHA-256 of the archived text: 0cca3c3935c819b32ae063cbab000cbb207d197939e45ba71e178a709ff2e3c8
Stable URL of this page: https://metraipolis.com/documento/dc_4bad91c3cb69

How to cite

Training-Free Hold-Usage Detection in Sport Climbing with Foundation Pose Models — arXiv:2609.30026 — arXiv (en), https://arxiv.org/abs/2609.30026 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4bad91c3cb69 (Metraipolis archive dc_4bad91c3cb69, sha256 0cca3c3935c819b32ae063cbab000cbb207d197939e45ba71e178a709ff2e3c8)

BibTeX

@misc{dc4bad91c3cb69,
  title = {Training-Free Hold-Usage Detection in Sport Climbing with Foundation Pose Models — arXiv:2609.30026},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.30026},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4bad91c3cb69; sha256 0cca3c3935c819b32ae063cbab000cbb207d197939e45ba71e178a709ff2e3c8},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.30026. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Detecting which holds a climber uses, and when, underpins automated scoring, movement analysis, and assistive systems for sport climbing. Existing approaches train task-specific models or repurpose 2D pose estimators whose hand keypoint sits at the wrist and foot keypoint at the ankle i.e. offset from the fingertips and toes that actually contact the holds, and whose hands are occluded in roughly half of all frames. We show that a frozen, off-the-shelf pose foundation model is sufficient: using the fingertip and toe keypoints of Sapiens, a per-frame proximity test against the annotated holds, per-limb mutual exclusion, and a short temporal-persistence rule, we detect hold usage without any climbing-specific training. On the The Way Up dataset (22 videos, 10 athletes, two routes), our method reaches an event F_1 of 90.2% on a held-out split (89.8% under leave-one-participant-out cross-validation) and 79.9% over all 22 videos at any temporal overlap, and performs best on footholds (F_1,89.8% overall, 96.6% held-out). Under an identical protocol it exceeds our reproductions of the YOLOv8-pose and ViTPose pipelines at every temporal threshold, with the margin widening under strict timing. An ablation shows that two intuitively helpful additions---dense foundation-feature change gating and body-part segmentation---both hurt, arguing that a minimal, keypoint-only design is the right one for this task. Finally, standard coaching statistics computed from our automatic predictions track ground truth closely (Pearson r=1.00 for climb time, 0.94 for pace), turning ordinary single-camera video into reliable performance metrics with no instrumentation.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-25 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.30026 — 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 2026-10-08T06:09:29.902Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-25?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).