Skeleton-based driver action recognition using ResGGCNN
Bibliographic record
Abstract
Driver action recognition serves a crucial role in assessing a driver’s distraction and readiness to takeover control in Level 3 of autonomy. In this study, we proposed an effective, driver-independent graph convolutional neural network (GCNN) using the skeletal representation by comprising determinative joints and inter-joint distances in the driver’s body structure. The proposed technique eliminates superfluous information. To distinguish various action classes, our proposed GCNN leverages residual gated connections (ResGGCNN), which enables the model to learn features efficiently at multiple levels. The proposed ResGGCNN is superbly lightweight with only almost 110k learnable parameters, makes it adequate for low-latency and embedded deployment. We evaluated our proposed model on the 3MDAD dataset with 16 common driving and non-driving related activities and achieved significant improvement in the recognition accuracy of driver’s action at the rate of 59.71% on RGB front-view modality.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".