Automated Pedestrian Detection: State of the Art
Bibliographic record
Abstract
A 2008 FHWA (Federal Highway Administration) Pedestrian Safety Report to Congress emphasized the potential of automated (or passive) pedestrian detection to improve safety. However, it also found that these technologies “require additional research and extensive field testing to demonstrate and evaluate the benefits of deploying the systems.” It pointed to concerns about costs and reliability, as well as the gap between limited U.S. experience and broader European and Australian acceptance of these devices. A recent on-line survey of local agencies (mostly in the U.S.) conducted by the University of Manitoba Transport Information Group and the ITE Technical Committee on Automated Pedestrian Detection also found a high level of concern about reliability and maintenance needs. However, most respondents did not report personal experience installing or evaluating these detectors for pedestrian signal or warning device applications. This paper presents preliminary findings of that ITE Committee, which is developing an informational report describing the range and effectiveness of such devices. The committee is also assessing liability, maintenance, and accessibility issues. This committee work comes at an important juncture. There is increasing interest in promoting safe walking to improve public health, promote air quality, and reduce congestion. Proposed MUTCD (Manual on Uniform Traffic Control Devices) amendments requiring longer signalized crossing times may also increase agency interest in using automated detection of pedestrians to “fine tune” crossing times to individual walking speeds.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".