A novel human posture estimation using single depth image from Kinect v2 sensor
Bibliographic record
Abstract
In this paper, we propose an approach to estimate general posture of the human-body. We perform various 2D scans of the body to carry out their Fast-Fourier-Transform(FFT) to extract features which can be fed to a 2-layer feed-forward neural-network. This approach can offer an effective procedure given limited resources which are usually available for deep learning approaches. In comparison with the literature, our proposed method doesn't require any specific skeleton points of the human body, results in a reduced level of computational complexities. We compared our method with the state-of-the-art and the results show an increased level of classification accuracy. The training dataset is captured from a single subject moving with various postures. If the test-data is also captured from the same subject, then the overall level of accuracy is 98.2% while if the test images are captured from different subjects then the accuracy of the estimation would be near 91.5%. We also test our method in the restricted field of view where parts of the subject's body are not in the field of sensor view, the result shows that in this case, the accuracy is as high as 79.1%.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".