Efficient ghost removal in motion detection with patch-corrected background differentiation
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
An efficient ghost removal technique is proposed as an extension to adaptive background differentiation for motion detection. The pixels of the first frame in the sequence representing moving objects are replaced with the values taken for the same pixels from the memory bank where those pixels are identified as non-moving ones. The memory bank is built of the frames immediately following or, alternatively preceding, the initial frame of the analyzed sequence. This allows creating the initial background model with no moving pixels. Parameters optimization is conducted for specific case of traffic control system application. Experiments demonstrate that threshold reduction is beneficial to achieve completeness of the ghost removal. Additionally, a second improvement is introduced to reduce for the noise by non-stationary cameras which are shown to be efficiently compensated by a second derivative in the temporal differentiation when working with videos at a sufficiently high frame rate.
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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 | 0.000 | 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 it