Automatic camera control using unobtrusive vision and audio tracking
Bibliographic record
Abstract
While video can be useful for remotely attending and archiving meetings, the video itself is often dull and difficult to watch. One key reason for this is that, except in very high-end systems, little attention has been paid to the production quality of the video being captured. The video stream from a meeting often lacks detail and camera shots rarely change unless a person is tasked with operating the camera. This stands in stark contrast to live television, where a professional director creates engaging video by juggling multiple cameras to provide a variety of interesting views. In this paper, we applied lessons from television production to the problem of using automated camera control and selection to improve the production quality of meeting video. In an extensible and robust approach, our system uses off-the-shelf cameras and microphones to unobtrusively track the location and activity of meeting participants, control three cameras, and cut between these to create video with a variety of shots and views, in real-time. Evaluation by users and independent coders suggests promising initial results and directions for future work.
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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 | 0.004 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".