Moving object detection from moving platforms using Lagrange multiplier
Bibliographic record
Abstract
Moving object detection is the first key step for many automated vision analysis applications. One of the major challenges to achieve accurate moving object detection is detecting moving objects in videos captured by moving camera platforms, also called active cameras, where both interest objects and background elements are moving. This paper presents a novel algorithm for moving objects detection from active cameras. The proposed method decomposes a video from an active camera into three components: background, moving objects, and transformation matrix between consecutive frames. The proposed method formulates the problem as a robust principle component analysis (PCA) problem (low rank matrix optimization problem) and solves it using inexact augmented Lagrange multiplier (IALM). In the proposed method, the background represents the low rank matrix, and the moving objects and transformation matrix are treated as added corruption. The robustness of the proposed method is demonstrated using a challenging dataset captured by camera mounted on unmanned air vehicle. The obtained results show that the proposed method achieves best results compared to other current state-of-the-art relevant 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".