A Stereo Vision-Based Approach to Marker-Less Motion Capture for On-Site Kinematic Modeling of Construction Worker Tasks
Bibliographic record
Abstract
Marker-less motion capture has been extensively studied in recent years as a means of evaluating productivity, safety, and workplace design for manual operations on-site. These technologies are ideal for circumstances in which traditional motion capture systems are ineffective due to the need for a laboratory setting and movement-inhibiting markers or sensors. However, many marker-less motion capture systems rely on RGB-D sensors that have limited range and susceptibility to interference from sunlight and ferromagnetic radiation, making them unsuitable for modeling worker actions on construction sites. To address this issue, we propose a marker-less motion capture approach utilizing optical images and depth data obtained from stereo vision cameras. Multiple camera lenses and triangulation algorithms generate depths maps similar to those produced by RGB-D sensors, while still utilizing an optical recording process unhindered by potentially harsh construction site conditions. These data are adapted for existing kinematic modeling systems (i.e. iPiMocap Studio) for 3-D pose estimation. The experiments show that the proposed approach can provide data precision comparable to that of RGB-D-based systems with fewer operational constraints; thus, motion data can be collected where previously developed methods fail due to environmental or maneuverability restrictions. With the proposed approach, kinematic modeling of human movements can be carried out on construction sites without inhibiting the mobility of the recorded subject.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".