Object detection using a moving camera under sudden illumination change
Bibliographic record
Abstract
In the recent years, various background subtraction methods have been proposed and used in vision systems for moving object detection and tracking; however most of them are sensitive to illumination change and have difficulty in handling shading and shadows caused by illumination change. Although there are some algorithms to handle illumination change, they need time on the order of several frames to estimate and train the background model and, in the majority of surveillance applications, there is no such time especially when the continuous detection of moving objects after a sudden illumination change is required or if objects of interest move fast. This paper presents a robust background subtraction method which is able to cope with sudden illumination change. Our algorithm is based on the key observation that statistical background model used for object detection right after a sudden illumination change can be inferred from the model before the change sufficiently accurately to allow continued detection without delay for model re-training. The algorithm was tested on both indoor and outdoor video sequences from different datasets. Experimental results show this approach works better than the state-of-the-art algorithms in background subtraction.
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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 | 0.002 | 0.001 |
| 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) | 0.000 | 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 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".