Bibliographic record
Abstract
In this paper we introduce our method for enabling dynamic gesture recognition for hand gestures. Like a number of other research work focusing on gesture recognition we use a camera to track the motions and interpret these in terms of actual meaningful gestures; however we emphasise the tracking of fingers as well as the hand in order to cover a much wider range of gestures. The recognition is processed as part of three key stages, with a fourth in development. The first stage processes the visual information from the camera, and identifies the key regions and elements (such as the hand and fingers), this classified information is passed to a 2D to 3D module that transforms the 2D classified information into a full 3D space applying it to a calibrated hand model using inverse projection matrices and inverse kinematics. Simplifying this model into posture curvature information we apply this to a hidden Markov model (HMM). This model is used to identify and differentiate between different gestures, even ones using the same finger combinations. We briefly discuss our current development in the application of context awareness to this scenario, which is used in combination with the HMM in order to apply a different semantic to each gesture. This is especially useful due to the huge overlap in semantics specifically appropriated to hand gestures
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.017 |
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".