Depth imaging system for human posture recognition
Bibliographic record
Abstract
This paper presents a human posture recognition system based on depth imaging. The proposed system is able to efficiently model human postures by exploiting the depth information captured by an RGB-D camera. Firstly, a skeleton model is used to represent the current pose. Human skeleton configuration is then analyzed in the 3D space to compute joint-based features. Our feature set characterizes the spatial configuration of the body through the 3D joint pairwise distances and the geometrical angles defined by the body segments. Posture recognition is then performed through a supervised classification method. To evaluate the proposed system we created a new challenging dataset with a significant variability regarding the participants and the acquisition conditions. The experimental results demonstrated the high precision of our method in recognizing human postures, while being invariant to several perturbation factors, such as scale and orientation change. Moreover, our system is able to operate efficiently, regardless illumination conditions in an indoor environment, as it is based depth imaging using the infrared sensor of an RGB-D camera.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".