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Record W3035376002 · doi:10.1109/cvprw50498.2020.00453

A System for Acquisition and Modelling of Ice-Hockey Stick Shape Deformation from Player Shot Videos

2020· article· en· W3035376002 on OpenAlexaff
Kaustubha Mendhurwar, Gaurav Handa, Leixiao Zhu, Sudhir P. Mudur, Etienne Beauchesne, Marc LeVangie, Aiden Hallihan, Abbas Javadtalab, Tiberiu Popa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSports Dynamics and Biomechanics
Canadian institutionsConcordia University
Fundersnot available
KeywordsShot (pellet)Ice hockeyPoint cloudDeformation (meteorology)Computer scienceComputer visionArtificial intelligencePoint (geometry)Computer graphics (images)GeometryGeologyMathematicsMaterials science

Abstract

fetched live from OpenAlex

In Ice-Hockey, a player shot significantly deforms the hockey-stick. Since this deformation plays a dynamic role in determining the flight of the puck, it is used in the study of hockey stick shapes, material properties, match to player style, etc. Reconstructing the deformable 3D shape of the stick during the course of a player shot has important applications. In this work we present a new, low cost, portable system to acquire videos of a player shot and to automatically reconstruct the deformation in 3D shape of the stick. The point clouds obtained are low resolution and noisy, as it is difficult to separate players hand geometry from the stick. We use the medial axis to constrain the point cloud to stick only geometry, and then use physics-based co-rotational FEM to determine the stick bend. We have tested the system with different sticks, players and shot styles, and our system yields accurate reconstructions. The results are discussed both qualitatively and where possible, quantitatively.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.542
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.195
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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