Statistical Cue Integration for Foveated Wide-Field Surveillance
Bibliographic record
Abstract
Reliable wide-field detection of human activity is an unsolved problem. The main difficulty is that low resolution and the unconstrained nature of realistic environments and human behaviour make form cues unreliable. Here we argue that reliability in far- or wide-field detection can still be achieved by probabilistic combination of multiple weak but complementary visual cues that do not depend on detailed form analysis. To demonstrate, we describe a real-time Bayesian algorithm for localizing human activity in relatively unconstrained scenes, using motion, background subtraction and skin colour cues. Fast sampling of scale space is achieved using integral images and a flexible norm that can handle sparse cues without loss of statistical power. We show that the probabilistic approach far outperforms a representative logical approach in which skin and background subtraction classifiers are combined conjunctively. Our method is currently used in a pre-attentive human activity sensor, generating saccadic targets for an attentive foveated vision system that reliably fixates faces over a 130 deg field of view, allowing high-resolution capture of facial images over a large dynamic scene.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".