A photogrammetric application in virtual sport training
Bibliographic record
Abstract
Abstract The paper discusses an application of close range photogrammetry for the development of a virtual training system for rugby football, and the use of the technique for the evaluation of rugby players’ performance. NuView, a stereo‐imaging device, and a digital high‐definition video (HDV) camera were used to capture stereoscopic video footage of players during field training. The left view and the right view were colour‐tinted cyan and red, respectively. The tinted stereo (anaglyph) views were projected onto a white screen, and players were instructed to practise ball‐throwing at the screen. A custom‐built laser device (TAM) measured the accuracy of the virtual throws. In addition, a photogrammetric system was used to track the movement of body segments, for example, the angle of shoulder orientation and the trunk flexion of the thrower. The measurements determined the parameters needed for an accurate throw and these parameters would be used in the training of new players. The study shows statistically significant differences in the values of these parameters between experienced and inexperienced players.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".