Towards 3D human posture estimation using multiple kinects despite self-contacts
Bibliographic record
Abstract
We present a marker-less human motion capture system that uses multiple RGB-D cameras to estimate the 3D posture of multiple people online at interactive rates in an indoor workspace measuring approximately 5 m × 5 m × 2 m. An interesting aspect of this work is how we handle the self-contact problem. We propose a novel multi-view voting scheme (MVS) to fuse measurements from different 2D or 3D algorithms. As a proof of concept, we present an MVS implementation that fuses optical flow images from each view and labels points in the current instance using the previously estimated posture. These labels allow us to trim invalid edges in a geodesic distance graph model and improve localization of geodesic extrema corresponding to the head, hands and feet for posture estimation. The system performs at ∼8.3 Hz with a cumulative latency of ∼570.40 ms and a projected median localization error of ∼0.149 m. In addition, we propose a new multi-view Kinect and Vicon publicly accessible motion capture dataset for validation and benchmarks.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".