A unified threshold updating strategy for multivariate Gaussian mixture based moving object detection
Bibliographic record
Abstract
Moving object detection is vitally used in video surveillance applications. Traditional Gaussian mixture model (GMM) based background subtraction (BGS) methods are usually performs well when background is stationary. However, they require parameter tuning to deal with dynamic backgrounds, whose background pixel values change over time. Particularly, the threshold which determines the pixels associated with moving objects from the resultant of BGS. To tackle this problem there is no ultimate solution. Considering that, this paper intents to present a novel idea to update the threshold of GMM based BGS with respect to color distortion, similarity and illumination measures in pixel level. Extensive experiments were carried out to demonstrate the effectiveness of the proposed method in comparison to some of the long-familiar GMM based BGS methods in literature. However, note that this paper is not attempted to provide a real-time technique, but rather to investigate the potential utilization of the aforementioned measures to set a threshold automatically to detect moving objects in video 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".