A general framework for extension of a tracking range of user-calibration-free remote eye-gaze tracking systems
Bibliographic record
Abstract
Stereo-camera Remote Eye-Gaze Tracking (REGT) systems can provide calibration-free estimation of gaze. However, such systems have a limited tracking range due to the requirement for the eye to be tracked in both cameras. This paper presents a general framework for extension of a tracking range of stereo-camera user-calibration-free REGT systems. The proposed method consists of two distinct phases. In the brief initial phase, estimates of eye-features [the center of the pupil and corneal reflections] in pairs of stereo-images are used to estimate automatically a set of subject-specific eye parameters. In the second phase, these subject-specific eye parameters are used with estimates of eye-features in images from any one of the systems' cameras to compute the Point-of-Gaze (PoG). Experiments were conducted with a system that includes two cameras in a horizontal plane. The experimental results demonstrate that the tracking range for horizontal gaze directions can be extended by more than 50%: from ±23.2° when the two cameras are used as a stereo pair to ±35.5° when the two cameras are used independently to estimate the PoG. By adding more cameras to the system, the proposed framework allows further extension of the tracking range in both horizontal and vertical direction, while preserving a user-calibration-free status of a REGT system.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".