Unsupervised motion detection using a markovian temporal model with global spatial constraints
Bibliographic record
Abstract
In this work, we propose an unsupervised Bayesian model for the detection of moving objects from dynamic scenes. This unsupervised solution is a three-step approach that uses a statistical model of an interframe gradient norm field (as likelihood model) with a local regularization term (as prior model) combined with strong intraframe spatial constraints. In the first step, the spatial constraints are estimated by making an unsupervised Markovian spatial over-segmentation of two input frames. In the second step, the interframe gradient (derived from the input frames) is restored to minimize undesired noise. In the last step, an unsupervised Markovian temporal segmentation (with global spatial constraints) is performed to generate the desired motion label field. The maximum a posteriori (MAP) estimation of the label field associated with the spatial segmentations (in the first step) and the motion label field (in the third step) is performed by a classical Iterative Conditional Mode (ICM) algorithm. An Iterative Conditional Estimation (ICE) procedure is exploited for estimating the parameters of the spatial model and the region-constrained temporal model. This new statistical method of motion detection has been successfully applied to real dynamic scenes and seems to be well suited for the temporal detection of noisy image sequences.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".