Use of Continuous Zoom on Electro-Optical Imaging Systems: Comparisons between Automatic and Manual Target Tracking
Bibliographic record
Abstract
The use of continuous zoom in an electro-optical sensor system was investigated with respect to target tracking. Using a simulation of an operator-machine interface in an airborne multi-sensor surveillance system, targets were tracked by manually directing the sensor or by an automated tracker. It was hypothesized that frequency of using the continuous zoom would be higher in the manual tracking mode than in auto-tracking, and negatively correlated with tracking error. Sensor, and targets to be tracked, were either moving or stationary in three types of tracking scenarios. Results showed that the zoom function was used more often when tracking manually, although the way continuous zoom was used differed between the two tracking modes. Also, tracking error was lower when the zoom function was used in manual mode. Tracking error was additionally affected by whether or not the target and/or the sensor were moving or stationary. Results improve our understanding of the way complex sensor systems are used, and will assist in ascertaining whether providing a continuous zoom into optical imaging systems is of benefit to operators.
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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 | 0.002 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".