Adaptive Pooling of the Most Relevant Spatio-Temporal Features for Action Recognition
Bibliographic record
Abstract
This paper presents a model-based action recognition system that utilizes the Kinect 3D skeleton to construct adaptive spatio-temporal motion representations. The proposed method utilizes two features, namely the joint relative distance (JRD) and joint relative angle (JRA) to encode the spatio-temporal motion patterns of different skeletal joints. To evaluate the relevance of a particular joint-pair in representing an action class, we introduce a flatness measure that quantifies the level of engagement of the corresponding joint-pair in performing the action. The flatness measures computed for all skeletal joint-pairs are accumulated to construct a joint-pair relevance (JPR) matrix, which facilitates adaptive pooling of the most relevant spatio-temporal features to construct the final motion description for individual action classes. In addition, we propose a score level fusion of JRD and JRA features with a weighted dynamic time warping (DTW)-based matching scheme to effectively boost the overall recognition performance. In our experiments, the proposed method achieves better recognition performance than well-known existing methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".