Automatic Real-Time Fever Screening in a Thermal Video Surveillance System
Bibliographic record
Abstract
Automatic identification of elevated human body temperature has become a topic of interest in health-care monitoring systems. The use of a thermal imaging device recently gained in popularity for screening Covid-19 infected subjects in public transportation and public places, but can also be transposed to a variety of domains. Scientific studies support that certain thermal imaging systems can be used to reliably measure surface skin temperature in a stable environment. In this paper we propose a fever detection system that expands the capabilities provided with single thermal sensor by integrating two thermal cameras equipped with nonuniformity correction shutters and a blackbody temperature reference source. The system automatically detects a subject's face and forehead as well as the surface of the blackbody, compensates for the error arising as the person is not on the same plane as the blackbody, and triggers an alarm if the body temperature is higher than a predefined threshold. Experiments demonstrate that the fever detection system succeeds to achieve a confidence margin of ±0.43°C.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".