On The Identification of Snow Movements on Roads
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
One of the key components of an intelligent transportation systems is video-based automatic incident detection (AID). An AID system is able to detect incidents that require operator intervention. However, the accuracy of an AID system operating during the winter suffers from high false alarm rates due to the movement of snow on the roads. In this paper, a robust algorithm is proposed to detect moving snow in video streams and improve the rate of detection by having the AID system to reduce its sensitivity in the area that has snow movement. The proposed algorithm conducts glare processing, background generation & differencing, snow sample correlation and final snow map generation. The feasibility of the proposed algorithm has been evaluated using traffic videos captured from several cameras from the City of Calgary. This algorithm demonstrates accurate and real-time detection of moving snow
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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 | 0.000 |
| 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