Multimodel-Based Gait Recognition Method with Joint Motion Constraints
Bibliographic record
Abstract
The intricate and restricted movements of joints form the core of pedestrian gait characteristics, with these traits externally reflected through the overall and synchronized gait movements.Thus, identifying features of such coordinated motions significantly boosts the discriminative effectiveness of gait analysis.Addressing this, we have introduced a novel gait feature mining approach that amalgamates multi-semantic information, effectively utilizing the combined strengths of silhouette and skeleton data through a meticulously designed dual-branch network.This network aims to isolate coordinated constraint features from these distinct modalities.To derive the coordinated constraint features from silhouette data, we crafted a silhouette posture graph, which employs 2D skeleton data to navigate through the silhouette's obscured portions, alongside a specialized local micro-motion constraint module.This module's integration of feature maps allows for the detailed extraction of features indicative of limb coordination.Concurrently, for the nuanced extraction of joint motion constraints, we developed a global motion graph convolution operator.This operator layers the motion constraint relations of physically separate joints onto the human skeleton graph's adjacency matrix, facilitating a comprehensive capture of both local and overarching limb motion constraints.Furthermore, a constraint attention module has been innovated to dynamically emphasize significant coordinated motions within the feature channels, thus enriching the representation of pivotal coordinated motions.This advanced network underwent thorough training and validation on the CASIA-B dataset.The ensuing experimental outcomes affirm the method's efficacy, demonstrating commendable recognition accuracy and remarkable stability across varying viewing angles and dynamic walking conditions.
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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 | 0.001 |
| 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) | 0.003 | 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".