Video-rate eigenspace methods for position tracking and remote monitoring
Bibliographic record
Abstract
The use of principal component analysis is employed for visual position determination and simultaneously for remote visual monitoring. The position of a simple planar robot is visually tracked at video rates using eigenspace methods. The eigenspace image coefficients are simultaneously sent over the Internet to visually display the robot operation at a remote location. A set of basis eigenvectors are first determined using the Karhunen-Loeve Transform (KLT) using an off-line learning process. Once the learning phase is complete, the run-time performance of the eigenspace methods are shown to be fast enough to operate at video rates using off-the-shelf components. The eigenspace provides a compact representation that can be employed for rapid position determination and to provide minimum image reconstruction error for a given number of basis vectors. The computational speed, accuracy, and latency for position determination are experimentally determined. The experimental results show that the eigenspace methods perform well for position tracking and for remote monitoring.
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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 | 0.001 |
| 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".