StableFlow: A novel real-time method for digital video stabilization
Why this work is in the frame
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Bibliographic record
Abstract
Digital video stabilization is crucial in many applications such as object detection and tracking. It has been studied for decades yielding an extensive amount of literature in the field, however, current approaches suffer from either being computationally expensive or under-performing in terms of visual quality . In this paper, we present StableFlow, a novel real-time method that was inspired by the mass-spring-damper physical model. In StableFlow, a video frame is modelled as a mass suspended in each direction by a critically dampened spring and damper which can be fine-tuned to adapt with different shaking patterns. The proposed method is tested on video sequences that have different types of shakiness and diverse video contents. The obtained results are then compared to current state-of-the-art stabilization algorithms including Youtube stabilization and it is found that the proposed method significantly outperforms other algorithms in terms of visual quality while performing in real time.
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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 | 0.002 |
| 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