Dynamic Gesture Classification for Vietnamese Sign Language Recognition
Bibliographic record
Abstract
This paper presents an approach of feature extraction and classification for recognizing continuous dynamic gestures corresponding to Vietnamese Sign Language (VSL).Input data are captured by the depth sensor of a Microsoft Kinect, which is almost not affected by the light of environment.In detail, each gesture is represented by a volume corresponding to a sequence of depth images.The feature extraction stage is performed by dividing such volume into a 3D grid of same-size blocks in which each one is then converted into a scalar value.This step is followed by the process of classification.The wellknown method Support Vector Machine (SVM) is employed in this work, and the Hidden Markov Model (HMM) technique is also applied in order to provide a comparison on recognition accuracy.Besides, a dataset of 3000 samples corresponding to 30 dynamic gestures in VSL was created by 5 volunteers.The experiments on this dataset to validate the approach and that shows the promising results with average accuracy up to 95%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".