Quantifying and Recognizing Human Movement Patterns From Monocular Video Images—Part I: A New Framework for Modeling Human Motion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research into tracking and recognizing human movement has so far been mostly limited to gait or frontal posing. Part I of this paper presents a continuous human movement recognition (CHMR) framework which forms a basis for the general biometric analysis of continuous human motion as demonstrated through tracking and recognition of hundreds of skills from gait to twisting saltos. Part II of this paper presents CHMR applications to the biometric authentication of gait, anthropometric data, human activities, and movement disorders. In Part I of this paper, a novel three-dimensional color clone-body-model is dynamically sized and texture mapped to each person for more robust tracking of both edges and textured regions. Tracking is further stabilized by estimating the joint angles for the next frame using a forward smoothing particle filter with the search space optimized by utilizing feedback from the CHMR system. A new paradigm defines an alphabet of dynemes, units of full-body movement skills, to enable recognition of diverse skills. Using multiple hidden Markov models, the CHMR system attempts to infer the human movement skill that could have produced the observed sequence of dynemes. The novel clone-body-model and dyneme paradigm presented in this paper enable the CHMR system to track and recognize hundreds of full-body movement skills, thus laying the basis for effective biometric authentication associated with full-body motion and body proportions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it