Tracking All Road Users at Multimodal Urban Traffic Intersections
Bibliographic record
Abstract
Because of the large variability of road user appearance in an urban setting, it is very challenging to track all of them with the purpose of obtaining precise and reliable trajectories. However, obtaining the trajectories of the various road users is very useful for many transportation applications. It is particularly essential for any task that requires higher level behavior interpretation, including new safety diagnosis methods that rely on the observation of road user interactions without a collision and therefore do not require waiting for collisions to happen. In this paper, we propose a tracking method that has been specifically designed to track the various road users that may be encountered in an urban environment. Since road users have very diverse shapes and appearances, our proposed method starts from background subtraction to extract the potential a priori unknown road users. Each of these road users is then tracked using a collection of keypoints inside the detected foreground regions, which allows the interpolation of object locations even during object merges or occlusions. A finite state machine handles fragmentation, splitting, and merging of the road users to correct and improve the resulting object trajectories. The proposed tracker was tested on several urban intersection videos and is shown to outperform an existing reference tracker used in transportation research.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".