Improvement in Multi-Person 2D Pose Estimation: Applying Polar Representation in OpenPose
Bibliographic record
Abstract
Recent pose machines provide a relative accurate estimation in 2D real-time multi-person situations. In this work, we demonstrate an advanced open-pose design with a sequential stages of prediction and use of polar coordinate system. The main contribution of this paper is to denote a pose machine frame work based on the available open-pose model, which performs improvement in both efficiency and accuracy in image-dependent spatial models learning. We achieve this by considering additional information of image features with both a sequential structure of convolutional networks and the support of part affinity fields, as well as the advantages of using polar coordinate system, which efficiently predicting accurate estimates in multi-person cases. Our approach characterizes how the concept of part affinity fields can be used in key points connection. We perform competing methods on standard data sets including COCO data set, compare our result with several bottom-up approach and illustrate the result in straightforward ways.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".