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 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 | 0.001 |
| 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 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".