A video-based image processing system for the automatic implementation of the eye involuntary reflexes measurements involved in the Drug Recognition Expert (DRE)
Bibliographic record
Abstract
In order to detect drivers under the influence of substances such as alcohol or drugs, police officers use a standardized set of tests such as the Horizontal Gaze Nystagmus (HGN) test, the eye convergence test and the pupil’s reaction to light test. These tests are part of the more complete Drug Recognition Expert (DRE) procedures and are essentially applied to the eyes of a driver. These procedures are performed manually by law enforcement officers. The present work describes a video-based image processing system implementing the HGN test, the convergence test and the pupil’s dark room examinations test. This system generates visual stimuli and captures video sequences of the eyes following and reacting to these visual stimuli. The video sequences are processed and analyzed using feature extraction techniques. In the present study, the video-based image processing system is used to detect alcohol related intoxication. This system was tested in an experiment involving 32 subjects dosed to a blood alcohol concentration (BAC) in the interval of 0.04% to 0.22%. In order to demonstrate the effects of alcohol on eye signs comparisons are made between pre-dose and post-dose BAC.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".