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ARXIV:2604.01383 · SPORTS AI · SUBMITTED 03 APR · 20:30 UTC · FRESHNESS STALE
ARXIV:2604.01383SPORTS AISUBMITTED 03 APR · 20:30 UTCFRESHNESS STALESyed Ahsan Masud Zaidi · Lior Shamir · William Hsu · Scott Dietrich · Talha Zaidi · arXiv
A training-free AI pipeline for precise First Point of Contact localization in American football practice videos, enabling biomechanical analysis without labeled data.
Opportunity summary
Pain A training-free AI pipeline for precise First Point of Contact localization in American football practice videos, enabling biomechanical analysis without labeled data.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence partial
A training-free AI pipeline for precise First Point of Contact localization in American football practice videos, enabling biomechanical analysis without labeled data. Reliable biomechanical analysis, therefore, depends on spatiotemporal localization that identifies both the…
American football practice generates video at scale, yet the interaction of interest occupies only a brief window of each long, untrimmed clip. Reliable biomechanical analysis, therefore, depends on spatiotemporal localization that identifies both the…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. These results show that frame-accurate contact onset localization in real-world practice footage is feasible without task-specific training. A public repository is linked, so build…
Sports AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A training-free AI pipeline for precise First Point of Contact localization in American football practice videos, enabling biomechanical analysis without labeled data.
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Paper Pack
10.48550/arXiv.2604.01383A training-free AI pipeline for precise First Point of Contact localization in American football practice videos, enabling biomechanical analysis without labeled data.
Abstract
American football practice generates video at scale, yet the interaction of interest occupies only a brief window of each long, untrimmed clip. Reliable biomechanical analysis, therefore, depends on spatiotemporal localization that identifies both the interacting entities and the onset of contact. We study First Point of Contact (FPOC), defined as the first frame in which a player physically touches a tackle dummy, in unconstrained practice footage with camera motion, clutter, multiple similarly equipped athletes, and rapid pose changes around impact. We present GRAZE, a training-free pipeline for FPOC localization that requires no labeled tackle-contact examples. GRAZE uses Grounding DINO to discover candidate player-dummy interactions, refines them with motion-aware temporal reasoning, and uses SAM2 as an explicit pixel-level verifier of contact rather than relying on detection confidence alone. This separation between candidate discovery and contact confirmation makes the approach robust to cluttered scenes and unstable grounding near impact. On 738 tackle-practice videos, GRAZE produces valid outputs for 97.4% of clips and localizes FPOC within $\pm$ 10 frames on 77.5% of all clips and within $\pm$ 20 frames on 82.7% of all clips. These results show that frame-accurate contact onset localization in real-world practice footage is feasible without task-specific training.
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partial0 refs; 0 sources; 50% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
A training-free AI pipeline for precise First Point of Contact localization in American football practice videos, enabling biomechanical analysis without labeled data. Reliable biomechanical analysis, therefore, depends on spatiotemporal localization that identifies both the int...
METHOD
American football practice generates video at scale, yet the interaction of interest occupies only a brief window of each long, untrimmed clip. Reliable biomechanical analysis, therefore, depends on spatiotemporal localization that identifies both the interacting entities and th...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. These results show that frame-accurate contact onset localization in real-world practice footage is feasible without task-specific training. A public repository is linked, so build verification can inspec...
WHY NOW
Sports AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A training-free AI pipeline for precise First Point of Contact localization in American football practice videos, enabling biomechanical analysis without labeled data. Reliable biomechanical analysis, therefore, depends on spatiotemporal localization that identifies both the interacting entities and the onset of contact.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
American football practice generates video at scale, yet the interaction of interest occupies only a brief window of each long, untrimmed clip. Reliable biomechanical analysis, therefore, depends on spatiotemporal localization that identifies both the interacting entities and the onset of contact.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. These results show that frame-accurate contact onset localization in real-world practice footage is feasible without task-specific training. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Sports AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A training-free AI pipeline for precise First Point of Contact localization in American football practice videos, enabling biomechanical analysis without labeled data.
Segment
Sports AI
Adoption evidence
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Commercial read
7.0/10 public viability
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missing
reason
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proof status
unverified
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confidence low
next verification path
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passport absent
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GitHub and Hugging Face maturity payloads
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stale
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Technical feasibility
partial
Current read
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Run minimal reproduction from the Build Passport prototype path.
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missing
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Write integration checklist from prototype path and target workflow.
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Classify regulatory flags before commercialization planning.
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ARTIFACTS
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DEFENSIBILITY
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