Calibration of Availability and Safety of a Video-Based Detection System for Airport APMs
Bibliographic record
Abstract
Most modern trains are equipped with onboard CCTV technology, which can be used for a wide variety of applications, such as automatic detection of objects, people, or situations by video analytics. Such as with all detection systems, thorough calibration of the sensors, i.e., cameras in our case, is essential to minimize false alarms and achieve a very high detection rate. From an operational point of view, false alarms should be minimal or not occur at all. From a reliability point of view, no real objects or people shall be missed, especially when it comes to airport security. Unfortunately, operational availability and detection reliability are contradictory parameters. They both depend on the chosen sensitivity of the sensors. Typically, high sensitivity results in a high detection rate (which is desired) but can produce a couple of false alarms. While a low sensitivity will reduce the number of false alarms but some smaller objects may not be detected. This paper presents an approach to analyze the detection performance and choose suitable parameter settings to satisfy both.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".