Dual Gaussian mixture model with pixel history for background suppression
Bibliographic record
Abstract
Background suppression has found several application areas in computer vision. Among others, abandoned object detection and moving object detection under dynamic backgrounds are two major application areas. Gaussian mixture model (GMM) is one of the most popular methods and has been applied in both of these application areas. However, the aforementioned areas present contradicting challenges for GMM. Abandoned objects need the GMM to slowly get adapted to prevent accidental assimilation of the foreground, while dynamic backgrounds need quick assimilation to prevent noisy detection. A novel dual GMM based on pixel history is proposed to provide practical solutions to both of these contradictory challenges. The method uses separate GMMs to represent foreground and background modes. A foreground mode is transferred to the GMM representing the background based on its history of occurrence. Thus, a quick assimilation is offered to dynamic backgrounds while abandoned objects face a very slow assimilation. In each case, old background is preserved. Extensive experiments are done to demonstrate the effectiveness of the proposed method and to compare it with a number of well known methods in literature.
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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 | 0.001 | 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 | 0.000 |
| 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".