Recognition and Difference Analysis of Human Walking Gaits Based on Intelligent Processing of Video Images
Bibliographic record
Abstract
Based on the residual network and long short-term memory (LSTM) network, this paper proposes a human walking gait recognition method, which relies on the vector image of human walking features and the dynamic lower limb model with multiple degrees-of-freedom (DOFs). Firstly, a human pose estimation algorithm was designed based on deep convolutional neural network (DCNN), and used to obtain the vector image of human walking features. Then, the movements of human lower limbs were described by a simplified model, and the dynamic eigenvectors of the simplified model were obtained by Lagrange method, revealing the mapping relationship between eigenvectors in gait fitting. To analyze the difference of human walking gaits more accurately, a feature learning and recognition algorithm was developed based on residual network, and proved accurate and robust through experiments on the data collected from a public gait database.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".