Camera Calibration for Urban Traffic Scenes: Practical Issues and a Robust Approach
Bibliographic record
Abstract
Video-based collection of traffic data is on the rise. Camera calibration is a necessary step in all applications to recover the real-world positions of the road users of interest that appear in the video. Camera calibration can be performed based on feature correspondences between the realworld space and image space as well as appearances of parallel lines in the image space. In urban traffic scenes, the field of view may be too limited to allow reliable calibration based on parallel lines. Calibration can be complicated in the case of incomplete and noisy data. It is common that cameras monitoring traffic scenes are installed before calibration was undertaken. In this case, laboratory calibration, which is taken for granted in many current approaches, is impossible. This work addresses various real world challenging cases, for example when only video recordings are available, with little knowledge on the camera specifications and setting location, when the orthographic image of the intersection is outdated, or when neither an orthographic image nor a detailed map is available. A review of the current methods for camera calibration reveals little attention to these practical challenges that arise when studying urban intersections to support applications in traffic engineering. This study presents the development details of a robust
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".