GPU-Accelerated Foveation for Video Frame Rate Tracking
Bibliographic record
Abstract
A new approach for acceleration of motion tracking in video using a combination of foveation and CUDA technology is described. The use of GPU-accelerated foveation allows motion segmentation to be performed at high frame rates on high-resolution video sequences. To illustrate the technique, the implementation of an optical flow algorithm and its application to motion-segmented video for the real-time visual position-servo of a robotic manipulator is provided. Mapping of the foveated motion segmentation algorithm to a 240 processor GPU is illustrated and the performance of the algorithm is characterized with examples of both synthetic and real data. The non-foveated segmentation algorithm is shown to have a significant performance increase over a single-threaded CPU application and the foveated-based segmentation is found to give an additional performance gain of up to 27× over non-foveated optical flow.
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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 | 0.000 | 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".