Motion vector search for 2.5D modeling of moving objects in a video scene
Bibliographic record
Abstract
If we can estimate accurately the rotational and translational motion of a moving object in 2D images then we will be able to construct 3D model from 2D images, which is known as structure from motion (SFM). This paper modifies the MPEG like research technique in order to determine accurately the motion vectors associated with the moving objects in a video sequence. MPEG like search techniques applied to the RGB images does not necessarily mean geometrically similar points. The accuracy of the motion vectors is low in areas where the color appears uniform. We modified MPEG technique as follows. First, We employed exhaustive search to find where an object moved between two adjacent frames, the reference frame and the target frame using the minimum square difference (MSD) for the RGB images. Second for each frame we find the corresponding black and white (BW) image and we combine it with the gradient images. We apply the MSD to the combined images. Finally we combine the results of the MSD produced from the RGB images and the gradient and BW images. We found that the combined method achieved better motion prediction than either RGB alone or gradient alone.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".