A Solution for Cross-Calibration of Gaze Tracker System and Stereoscopic Scene System
Bibliographic record
Abstract
There is evidence that shows that the majority of all vehicle accidents are caused by human error. This has been the major motivation for advancements in Advanced Driving Assistance Systems (ADAS). A driver's gaze can provide valuable information about the focus and intention of a driver. Therefore, determining the degree of driver awareness and scene perception, and predicting driver intentions can be valuable in the next generation of ADAS. To this end, we have instrumented a vehicle with a front-facing stereoscopic vision system on the roof of the vehicle and a camera gaze tracking system pointing toward the driver's face. These are two completely different systems with dissimilar sensing modalities. Data is collected separately from these systems and when calibrated can be used to estimate the Point-of-Gaze (PoG) of the driver. We present an efficient approach for the cross-calibration of the gaze tracker with the stereoscopic vision system. The experimental results show that our proposed cross-calibration technique obtains promising results for estimating Point-of-Gaze (PoG).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".