Improving multiple target tracking in structured environments using velocity priors
Bibliographic record
Abstract
In this paper, we present an algorithm for determining a velocity probability distribution prior from low frame rate aerial video of an urban area, and show how this may be used to aid in the multiple target tracking problem, as well as to provide a foundation for the automated classification of urban transportation infrastructure. The algorithm used to develop the prior is based on using a generic interest point detector to find automobile candidate locations, followed by a series of filters based on scale and motion to reduce the number of false alarms. The remaining locations are then associated between frame pairs using a simple matching algorithm, and the corresponding tracks are then used to build up velocity histograms in the areas that are moved through between the track endpoints. The algorithm is tested on a dataset taken over urban Tucson, AZ. The results demonstrate that the velocity probability distribution prior can be used to infer a variety of information about road lane directions, speed limits, etc..., as well as providing a means of describing environmental knowledge about traffic rules that can be used in tracking.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".