Estimating pedestrian walking speed at street crossings using the YOLOv4 and deep SORT algorithms: Proof of principle
Bibliographic record
Abstract
There is evidence that existing standards for signal timing do not provide enough time for many pedestrians to safely cross intersections. Yet, current methods for studying this problem rely on inefficient manual observations. The objective of this work was to determine if the YOLOv4 and Deep SORT computer vision algorithms have the potential to be incorporated into automated measurement systems to measure and compare pedestrian walking speeds at one-stage and two-stage street crossings captured in birds-eye-view video. Walking speed was estimated for 1018 pedestrians at single-stage (591 pedestrians) and two-stage (427 pedestrians) street crossings. Pedestrians in the one-stage crossing were found to be significantly slower than pedestrians who crossed the two-stage crossing in one signal (1.19 ± 0.50 vs. 1.31 ± 0.49 m/s, p < 0.001). This proof of principle study demonstrated that the YOLOv4 and Deep SORT approaches are promising for estimating pedestrian walking speed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".