Deep learning-based 2D keypoint detection in alpine ski racing – A performance analysis of state-of-the-art algorithms applied to regular skiing and injury situations
Bibliographic record
Abstract
Objectives: In this study, we examined the practicability of deep learning-based 2D keypoint detection applied to regular skiing and injury situations (i.e., out-of-balance situations and fall situations) on an alpine ski racing track. Methods: We therefore created a regular skiing- and injury situation-specific dataset (hereinafter called "Injury Ski Dataset"), on which the state-of-the-art keypoint detection algorithms OpenPose, Mask-R-CNN, AlphaPose and DCPose were compared. The performance of each keypoint detector was evaluated by calculating the mean per joint position error (MPJPE) and the percentage of correct keypoints (PCK). Failure cases and common error patterns were further investigated by a visual analysis. Results: We observed the best results for regular skiing, with 81%-92% of all keypoints detected correctly at an MPJPE of 9 (2) to 14 (3) pixels. In injury situations, self-occlusions and rare poses became more likely, similar to occlusions due to snow spray and motion blur. As a result, the performance in out-of-balance situations decreased to 68%-80% (PCK), while in fall situations, only 35%-54% of all keypoints were detected correctly, with mean errors of 26-36 pixels. Among all algorithms, AlphaPose was the most robust and achieved the best results. Conclusions: PCK and MPJPE for regular skiing were in the range of manual annotation errors and can be considered low enough for further biomechanical analysis. For fall situations, keypoint detection should be further improved. Regarding the development of a deep learning tool for injury analysis in alpine skiing in the future, we propose to fine-tune a well-performing keypoint detector, such as AlphaPose, on a ski- and injury-specific dataset, such as ours.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".