An Improved Kernelized Correlation Filter for Extracting Traffic Flow in Satellite Videos
Bibliographic record
Abstract
In satellite video vehicle tracking, due to the tracking failure and tracking loss caused by similar characteristics of the target and obstacle occlusion, respectively, the traffic flow extraction accuracy is reduced. To address these issues, an improved traffic flow extraction method for satellite video based on kernelized correlation filter (KCF) was proposed. First, we introduced a multifeature fusion strategy into the KCF based on the discrete Fourier transform (DFT) framework to enhance vehicle tracking accuracy and reduce tracking drift and jumps. Second, we utilized the Kalman filter for trajectory prediction to reduce the loss of target during vehicle tracking. Compared with other mainstream algorithms on the satellite video dataset, the results showed that the tracking accuracy and success rate of the proposed method reached 86.74% and 79.96%, respectively. Finally, the virtual detection line method was used to extract the traffic flow. The experimental results showed that compared with the real traffic flow data obtained by visual method, the accuracy of satellite video traffic flow extraction by virtual detection line was 98.48% under noncongestion condition and 90.18% under congestion condition.
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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 | 0.000 |
| 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 | 0.000 | 0.002 |
| Open science | 0.000 | 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".