Camera Calibration for Urban Traffic Scenes: Practical Issues and a Robust Approach
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
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little knowledge on the camera specifications and setting location, when the orthographic image of the intersection is outdated, or when neither an orthographic image nor a detailed map is available.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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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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