Tracking the Line of Primary Gaze in a Walking Simulator: Modeling and Calibration
Bibliographic record
Abstract
This paper describes a system for tracking the Line of Primary Gaze (LoPG) of subjects as they view a large projection screen. LoPG is monitored using a magnetic head tracker and a tracking algorithm. LoPG tracking can also be combined with a head mounted eye tracker to enable gaze tracking. The algorithm presented uses a polynomial function to correct for distortion in magnetic tracker readings, a geometric model for computing LoPG from corrected tracker measurements, and a method for finding intersection of the LoPG with a screen. Calibration techniques for the above methods are presented. Results of two experiments validating the algorithm and calibration methods are also reported. Experiments showed an improvement in accuracy of LoPG tracking provided by each of the two presented calibration steps yielding errors in primary gaze-point measurements of less than two degrees over a wide range of head positions. AUTHOR'S NOTE This work was supported in part by National Institutes of Health Grant EY12890. Commercial eye tracker manufacturers, such as SR Research (Osgoode, ON Canada), ISCAN (Burlington, MA) and Applied Science Laboratories (ASL: Bedford, MA), market systems for monitoring point of regard on a display surface (combining head and eye tracking), but the manufacturers of these systems provide the devices as black-box tools. This makes it difficult for researchers to modify these trackers for special applications such as the large display and wide range of head movement needed for our walking simulator (Figure 1). This simulator takes the form of a projected virtual environment where a subject views computer generated images projected onto a large screen (Southard, 1995). Access to the algorithms and intermediate variables computed within the commercial gaze ...
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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 | 0.000 | 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 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".