An optical method for automated roadside detection and counting of vehicle occupants
Bibliographic record
Abstract
The monitoring and control of traffic volume is becoming a constant social, economic, and environmental pressure in the UK and elsewhere, because of landmass and current infrastructure strain under a swelling and increasingly mobile population. The viability of high-occupancy vehicle (HOV) lanes for easing traffic congestion, and hence maximising traffic flow, has been proven in countries worldwide. The USA, Australia, and Canada have had HOV installations for some time, controlling the flux of traffic into their most densely populated areas. Experience in these cases has dictated that it is enforcement which is crucial to the successful implementation of such a traffic policy. To date, all enforcement has been manual, i.e. a police officer counting the occupants in a vehicle as it passes by. Studies have concluded that manual enforcement is typically only 65 per cent accurate and, considering the pressures which one individual is put under in these circumstances, this statistic is not surprising. Lighting and environmental conditions, skin tone, and location of the occupants and the alertness of the officer are all variables affecting the accuracy of manually collected data. Hence there is the need for an autonomous detection system to count the occupancy within a vehicle. This could provide the basis of an enforcement or congestion strategy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.002 |
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".