Player tracking and identification of game systems in basketball using three cameras
Bibliographic record
Abstract
In this paper a new strategy for observing and analyzing a basketball match using video processing techniques to identify the game systems of a team is described. The system tracks players' positions during the match. At the outset, three video streams from three fixed cameras are available, each processed separately to deliver measures of the players' positions from different available views. Each treated view includes foreground detection and a bounding-box tracker designed to contain the pixels representing each player. During the multi-view process, measurements from different views are synchronized to enable identification of the same player when the player is visible simultaneously on several cameras. These measurements are combined in order to obtain the players' positions as well as their updated positions through the images. The position thus obtained is exploited in a database containing the representative points (coordinates) of all the players, who form a polygon. The analysis of a game system is thus simply the analysis of the deformation and movement of this polygon during the match. Comparative indicators of the two teams are defined, along with an indicator which represents the opinion of an expert in the sport (action code). Statistical tools are exploited with the objective on the one hand of identifying correlations and relationships between different indicators, and on the other hand of identifying the game system adopted by comparing the expert opinion and the results of the established heuristic models.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".