Camera selection using a local image quality metric for a distributed smart camera network
Why this work is in the frame
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Bibliographic record
Abstract
A set of camera selection templates has been developed and applied to a twelve-camera inward-looking distributed smart camera network mounted on the vertices of an icosahedral frame. These templates, designed using the fundamentals of self-organization, consist of simple rules based on a local (camera) level metric - a measure of the quality of detection of the target-of-interest for a given camera node based on a measurable target parameter. The effectiveness of the camera selections, based on the local metric, is analyzed through the performance of a global (system) level metric. The camera selection templates are shown to maintain a desirable global metric performance, while using a subset of the total available cameras. This holds true even when a single occluding target is introduced into the system.
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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 | 0.000 | 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) | 0.000 | 0.000 |
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 it