VEHICLE TRACKING AND SPEED ESTIMATION FROM UNMANNED AERIAL VIDEOS
Bibliographic record
Abstract
Abstract. In this paper, a solution for vehicle speed estimation using unmanned aerial videos is described. First, convolutional neural networks and Kalman filtering using deep features are used for detecting and tracking vehicles. Then, a photogrammetric approach is developed for estimating the three-dimensional (3D) position of the tracked vehicles on the road, which allows determining their speed. No assumptions are made about either the 3D structure of the road (e.g., constraining it to be a planar surface) or the camera pose (e.g., restricting it to be stationary). Therefore, this solution applies to videos acquired by a moving unmanned aerial vehicle from complex road structures (e.g., multi-level highways). This solution is also robust to changes of viewpoint and scale, which makes it applicable to situations where cars undergo orientation and resolution changes as observed from the sky (e.g., in roundabouts). Experiments showed that a high detection accuracy could be achieved with an F1-score of 94.54%. Besides, the tracking technique performed well, with a multiple-object tracking accuracy of 89.8% at a speed of 11 frames per second on videos of 2720×1530 pixels. Vehicle positioning (and thus, speed estimation) could be performed with an average accuracy of 0.6 m.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".