Cooperative Edge Inference and Virtual Simulation for Real-Time 3D Human Pose Estimation in Safety-Critical Applications
Bibliographic record
Abstract
Accurate and real-time 3D human pose estimation is essential for safety-critical applications such as intelligent surveillance, yet remains challenging in resource-constrained and dynamic environments due to its high computational demands. To address this, we propose a cooperative edge inference method for real-time 3D pose estimation in mobile edge computing networks. End devices equipped with lightweight models apply dual confidence thresholds to filter uncertain inputs, offloading only the selected images to an edge server for refined inference. We formulate a joint optimization problem to determine the optimal confidence thresholds and transmission times per device with the aim of minimizing the mean per-joint position error under end-to-end delay constraints. To evaluate the proposed method under controlled and repeatable multi-view conditions, we developed a virtual 3D simulation environment in Unity that mimics motion scenarios and provides accurate ground-truth pose data. Experimental results demonstrate a clear trade-off between accuracy and latency, and confirm that the proposed method significantly improves estimation accuracy while consistently meeting delay requirements.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".