Bibliographic record
Abstract
Most methods for automatic estimation of external camera parameters (e.g., tilt angle) from deployed cameras are based on vanishing points. This requires that specific static scene features, e.g., sets of parallel lines, be present and reliably detected, and this is not always possible. An alternative is to use properties of the motion field computed over multiple frames. However, methods reported to date make strong assumptions about the nature of objects and motions in the scene, and often depend on feature tracking, which can be computationally intensive and unreliable. In this paper, we propose a novel motion-based approach for recovering camera tilt that does not require tracking. Our method assumes that motion statistics in the scene are stationary over the ground plane, so that statistical variation in image speed with vertical position in the image can be attributed to projection. The tilt angle is then estimated iteratively by nulling the variance in rectified speed explained by the vertical image coordinate. The method does not require tracking or learning and can therefore be applied without modification to diverse scene conditions. The algorithm is evaluated on four diverse datasets and found to outperform three alternative state-of-the-art methods.
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 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 | 0.001 | 0.002 |
| Open science | 0.001 | 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".