Calibration techniques for object tracking using a compound eye image sensor
Bibliographic record
Abstract
Vanishing point and Z-tranform image center calibration techniques are reported for a prototype “compound-eye” camera system which can contain up to 25 “eyelets”. One application of this system is to track a fast-moving object, such as a tennis ball, over a wide field of view. Each eyelet comprises a coherent fiber bundle with a small imaging lens at one end. The other ends of the fiber bundles are aligned on a plane, which is re-imaged onto a commercial CMOS camera. The design and implementation of the Dragonfleye prototype is briefly described. Calibration of the image centers of the eyelet lenses is performed using a vanishing point technique, achieving an error of approximately ±0.2 pixels. An alternative technique, the Z-transform, is shown to be able to achieve similar results. By restricting the application to a two-dimensional surface, it is shown that similar accuracies can be achieved using a simple homography transformation without the need for calibrating individual eyelets. Preliminary results for object tracking between eyelets are presented, showing an error between actual and measured positions of around 3.5 mrad.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".