Localization and identification of body extremities based on data from multiple depth sensors
Bibliographic record
Abstract
This paper explores the novel use of multiple depth sensors to overcome occlusions and improve localization and tracking of body extremities. The usage of data from only depth sensors not only overcomes visual challenges associated with RGB sensors under low illumination, but also protects the identity of surveyed person with high confidentiality. For integrating depth information from multiple sources, the paper presents first an overview of a novel calibration method for multiple depth sensors. In case of occlusion of any fiducial point in the primary sensor's depth image, co-ordinates of the point can be obtained from the frame of other sensors using the calibration parameters. To localize salient body parts such as hands, head and feet, a surface triangular mesh is applied on generated 3D point cloud from the primary sensor. The geodesic extrema from the mesh coincide with body extremities. The body extremities can be identified based on those relative geodesic distances between the extremities. Once the body parts are labelled, a portion of body can be targeted and evaluated for specific gait analysis and visualization. For the performance evaluation, our calibration method has fared well in comparison to other available techniques. Also, our proposed localization of salient body parts is able to successfully tag the specific body part i.e. the head region.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".