3D Posture Estimation from 2D Posture Data for Construction Workers
Bibliographic record
Abstract
3D Posture Estimation from 2D Posture Data for Construction Workers Yantao Yu, Heng Li and Xincong Yang Pages 26-34 (2019 Proceedings of the 36th ISARC, Banff, Canada, ISBN 978-952-69524-0-6, ISSN 2413-5844) Abstract: Construction workers behaviour is important for safety, health and productivity management. Workers 3D postures are the data foundation of their behaviours. This paper established a preliminary 3D posture dataset of construction tasks and provided a 3D posture estimation method based on 2D joint locations. The results showed that the method could estimate 3D postures accurately and timely. The mean joint error and estimation time of each frame were 1.10 cm and 0.12 ms respectively. This method makes it possible to estimate construction workers 3D postures from construction site images and contributes to a data-based construction workers behaviour management. Keywords: Posture estimation; Construction worker; Behavior management DOI: https://doi.org/10.22260/ISARC2019/0004 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".