Pre-Attentive Face Detection for Foveated Wide-Field Surveillance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Conventional surveillance sensors suffer from an unavoidable tradeoff between image resolution and field of view. This problem may be overcome by combining a fixed, preattentive, low-resolution wide-field camera with a shiftable, attentive, high-resolution narrow-field camera. Here we present techniques for orienting the attentive camera to faces detected in the pre-attentive wide-field image stream. Unfortunately, the low image resolution of the widefield sensor precludes the use of most conventional face detection algorithms. Instead, we argue that reliable performance can best be achieved by accurate probabilistic combination of multiple cues: skin detection, motion detection and foreground extraction. Fast sampling of scale space over all three modalities is achieved using integral images and parametric models of response distributions are derived using supervised learning techniques. Log likelihood ratios for each modality are combined with spatial priors incorporating tracking and novelty objectives to yield a posterior map indicating the probability of a face appearing at each image location. The result is a real-time attentive visual sensor which reliably fixates faces over a 130 deg field of view, allowing high-resolution capture of facial images over a large dynamic scene
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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