Using physics-based methods to Model the Deformation of \nIce-Hockey Sticks from player shot videos
Bibliographic record
Abstract
In ice-hockey games, the hockey stick is usually deformed significantly when the players hit the puck. Different materials are used to make hockey sticks. Studies have been proposed to find how different hockey sticks and their deformation affect the performance of players. The reconstruction of a hockey stick's deformation during a shot is the key of many of those studies. The goal of this thesis is to model the deformation of the sticks from player shot videos using physics-based models and compare two physics-based methods for their performance on this problem. In this thesis, we propose two physics-based models to deform the stick and we integrate the models into a pipeline for stick reconstruction. The contribution of this thesis is twofold: 1) Integrate the physics-based models to the pipeline including data pre-processing, denoising, and establishing constraints. 2) Adapting, implementing, and comparing two physics-based models. We evaluate the results by overlapping the deformed template with reconstructed point clouds and comparing our results with the data from a MOCAP system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".