MOVING VEHICLE DETECTION USING A SINGLE SET OF QUICKBIRD IMAGERY — AN INITIAL STUDY
Bibliographic record
Abstract
Moving vehicle detection has become increasingly important in transportation management and road infrastructure development. State-of-the-art moving vehicle detection techniques require multi-frame images or at least a stereo pair of aerial photos for moving information extraction. This paper presents a new technique to detect moving vehicles using just one single set of QuickBird imagery, instead of using airborne multi-frame or stereo images. To find out the moving information of a vehicle, we utilize the unnoticeable time delay between multspectral and panchromatic bands of the QuickBird (or Ikonos) imagery. To achieve an acceptable accuracy of speed and location information, we first employ a refined satellite geometric sensor model to precisely register QuickBird multispectral and panchromatic bands, and then use a new mathematic model developed in our research to precisely calculate the ground position and moving speed of vehicles detected in the image. The initial testing results demonstrate that the technique developed in this research can extract information on position, speed, and moving direction of a vehicle at a reasonable accuracy. 1.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".