Road Boundary Detection in Challenging Scenarios
Bibliographic record
Abstract
This paper presents a new approach for automatic road detection in traffic cameras. The technique proposed here detects the dominant road boundary and estimates the vanishing point in images captured by traffic cameras under a wide range of lighting and environmental conditions, e.g., in images of unlit highways captured at night, etc. The approach starts by segmenting the traffic scene into a number of superpixel regions. The contours of these regions are used to generate a large number of edges which are organized into clusters of co-linearly similar sets using hierarchical bottom up clustering. A confidence level is assigned to each cluster using a statistical approach and the best clusters are chosen. Pairs of clusters with high confidence levels are then ranked and filtered according to image perspective and activity. The top ranked pair is selected as the road boundary. The proposed technique is tested on a real world dataset collected from the Ontario 401 traffic surveillance system. Experimental results demonstrate a distinct speedup and improvement in accuracy of the proposed technique in detecting the dominant road boundary in challenging scenarios compared to the state of the art Gabor filter based technique.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".