Localization of specific body part by multiple depth sensors network
Bibliographic record
Abstract
This paper explores tracking of specific body part by a network of multiple depth sensors. The usage of data from multiple depth sensors overcomes not only occlusion problems, but also visual challenges associated with RGB sensors under low illumination. Also, the identity of surveyed patient will be kept confidential as the silhouette of person is only shown in depth images. After determining relative poses between depth sensors, any point from depth image plane of any sensor can be transformed and projected onto depth image plane of another observing sensor. Such localization aid in overcoming occlusions observed in one sensor through obtaining information on the occluded parts from another sensor. Our paper introduces sensor scheduling system in which one of the multiple depth sensors' 3D co-ordinate frame is selected as world coordinate frame, contains fused point cloud and can track various limbs and parts such as head, hands and feet over time. To localize salient body extremities, a surface triangular mesh is applied on 3D fused point cloud with its corresponding generated geodesic extrema of that mesh coinciding with body extremities. The body extremities can be labelled based on those relative geodesic distances between body extremities. In evaluation, our multiple depth sensors system has managed to successfully localize specific body part i.e. head with less error against ground truth.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".