Use of Spatiotemporal Parameters of Gait for Automated Classification of Pedestrian Gender and Age
Bibliographic record
Abstract
This study investigates the feasibility of using the spatiotemporal parameters of gait—step frequency and step length—as cues for classifying pedestrians according to their gender and age. The gait parameters are automatically extracted from the pedestrian walking speed profile. Computer vision techniques are used for the automatic detection and tracking of pedestrians in an open (uncontrolled) environment. The classification is undertaken by using a simple k nearest neighbor algorithm. For demonstration, two case studies are used: Vancouver, British Columbia, Canada, and Oakland, California. For gender, correct classification rates of 78% and 81% were achieved for the Vancouver and Oakland case studies, respectively. Gender classification for the Vancouver case study considered pedestrians walking alone or in groups, and the Oakland case study gender classification considered only pedestrians walking alone. Pedestrian age classification resulted in a correct classification rate of 86% for the Oakland case study. Another classification measure, the kappa statistic, showed that the classification results were statistically significant beyond what is expected by chance. The method has the advantages of relying only on the pedestrian speed profile and using a simple classification algorithm.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".