A stereo camera based full body human motion capture system using a partitioned particle filter
Bibliographic record
Abstract
In this paper, we propose a marker-less full body human motion capture system designed for humanoid robot applications. The system is based on a stereo camera, and therefore has strong portability. Tracking is implemented within the particle filter framework, and the high dimensionality problem is solved through partitioned sampling. Taking advantage of the stereo setup, we propose a depth cue which resolves the problem of missing depth information in monocular tracking. Three other cues, the edge cue, the color cue and the distance cue, are also integrated into the system to enhance the tracking performance. The system is tested using the publicly available CMU MOCAP database which also includes ground truth data, and this enables us to analyze the results quantitatively and compare the relative usefulness of different cues. The system is shown to be capable of tracking challenging videos accurately and robustly in near real-time.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".