Object detection using a moving camera under sudden illumination change
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
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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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 | 0.002 |
| 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