Multibeam Forward-Looking Sonar Video Object Tracking Using Truncated - Sparsity and Aberrances Repression Regularization
Bibliographic record
Abstract
Multibeam forward-looking sonar (MFLS) video object tracking is a challenging problem due to the negative impacts of weak features and background clutter. In this letter, a novel multibeam forward-looking sonar video object tracking method via hybrid regularization scheme is proposed. The proposed regularization scheme is a composite method with truncated$\ell _{1}$-$\ell _{2}$sparsity regularization and aberrances repression regularization. While the truncated$\ell _{1}$-$\ell _{2}$sparsity regularization explores the structural sparsity of the learned filter to address background clutter, the aberrances repression regularization can alleviate the undesired spatial bounding effect. The resulting optimization problem is solved by alternating direction method of multipliers (ADMM). A proximal operator with truncated soft-thresholding scheme is proposed for the sub-problem with truncated$\ell _{1}$-$\ell _{2}$sparsity regularization. Experiments based on five multibeam forward-looking sonar videos for underwater docking validate the effectiveness of the proposed method, compared to other 8 state-of-the-art tracking methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".