Improving background subtraction using Local Binary Similarity Patterns
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most of the recently published background subtraction methods can still be classified as pixel-based, as most of their analysis is still only done using pixel-by-pixel comparisons. Few others might be regarded as spatial-based (or even spatiotemporal-based) methods, as they take into account the neighborhood of each analyzed pixel. Although the latter types can be viewed as improvements in many cases, most of the methods that have been proposed so far suffer in complexity, processing speed, and/or versatility when compared to their simpler pixel-based counterparts. In this paper, we present an adaptive background subtraction method, derived from the low-cost and highly efficient ViBe method, which uses a spatiotemporal binary similarity descriptor instead of simply relying on pixel intensities as its core component. We then test this method on multiple video sequences and show that by only replacing the core component of a pixel-based method it is possible to dramatically improve its overall performance while keeping memory usage, complexity and speed at acceptable levels for online applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.001 | 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 it