Automatic Detection of Deficient Video Log Images Using a Histogram Equity Index and an Adaptive Gaussian Mixture Model
Bibliographic record
Abstract
Abstract: Video log images are often used by transportation agencies to manually or automatically extract roadway infrastructure information, including roadway geometry, signs, etc. Poor-quality images, especially those having illumination-related deficiencies caused by color corruption with a plain-like grayscale histogram, sun glare, or darkness problems, are unacceptable and need to be identified. Manually reviewing the tens of millions of video log images for quality control is labor intensive and time-consuming, so there is a need to develop automatic video log image quality control procedures. The contribution of this article is that it formulates a new problem of roadway video log image quality control and then proposes a reasonable solution to address this problem in the hope that it will motivate the development of new algorithms by other researchers. For the first time, an algorithm using a Histogram Equity Index (HEI) and an adaptive Gaussian Mixture Model is proposed to address the video log image quality issue by automatically detecting illumination-related deficiencies. The Alberta Department of Transportation provided 15,489 video log images to test the proposed algorithm. Test results show that the developed algorithm can detect illumination-related video log image deficiencies with a false positive rate of 4%, 3%, and 12%; a false negative rate of 15%, 17%, and 19% for plain-like color corruption, dark, and sun glare conditions, respectively; computation time is 0.1 second/image. The proposed algorithm could potentially be used to improve video log image data quality control.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".