Localizing people in crosswalks with a moving handheld camera: proof of concept
Bibliographic record
Abstract
Although people or object tracking in uncontrolled environments has been acknowledged in the literature, the accurate localization of a subject with respect to a reference ground plane remains a major issue. This study describes an early prototype for the tracking and localization of pedestrians with a handheld camera. One application envisioned here is to analyze the trajectories of blind people going across long crosswalks when following different audio signals as a guide. This kind of study is generally conducted manually with an observer following a subject and logging his/her current position at regular time intervals with respect to a white grid painted on the ground. This study aims at automating the manual logging activity: with a marker attached to the subject’s foot, a video of the crossing is recorded by a person following the subject, and a semi-automatic tool analyzes the video and estimates the trajectory of the marker with respect to the painted markings. Challenges include robustness to variations to lighting conditions (shadows, etc.), occlusions, and changes in camera viewpoint. Results are promising when compared to GNSS measurements.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".