Temporal Synchronization of Video Sequences in Theory and in Practice
Bibliographic record
Abstract
In this work, we present a formalization of the video synchronization problem that exposes new variants of the problem that have been left unexplored to date. We also present a novel method to temporally synchronize multiple stationary video cameras with overlapping views that: 1) does not rely on certain scene properties, 2) suffices for all variants of the synchronization problem exposed by the theoretical disseration, and 3) does not rely on the trajectory correspondence problem to be solved apriori. The method uses a two stage approach that first approximates the synchronization by tracking moving objects and identifying inflection points. The method then proceeds to refine the estimate using a consensus based matching heuristic to find moving features that best agree with the pre-computed camera geometries from stationary image features. By using the fundamental matrix and the trifocal tensor in the second refinement step we are able to improve the estimation of the first step and handle a broader range of input scenarios and camera conditions.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".