Shadow Removal in Front Projection Environments Using Object Tracking
Why this work is in the frame
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Bibliographic record
Abstract
When an occluding object, such as a person, stands between a projector and a display surface, a shadow results. We can compensate by positioning multiple projectors so they produce identical and overlapping images and by using a system to locate shadows. Existing systems work by detecting either the shadows or the occluders. Shadow detection methods cannot remove shadows before they appear and are sensitive to video projection, while current occluder detection methods require near infrared cameras and illumination. Instead, we propose using a camera-based object tracker to locate the occluder and an algorithm to model the shadows. The algorithm can adapt to other tracking technologies as well. Despite imprecision in the calibration and tracking process, we found that our system performs effective shadow removal with sufficiently low processing delay for interactive applications with video projection.
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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 | 0.000 | 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 | 0.001 |
| 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