Bibliographic record
Abstract
The growing size of organizations is making it increasingly expensive to attend\nmeetings and difficult to retain what happened in those meetings. Meeting video\ncapture systems exist to support video conferencing for remote participation or\narchiving for later review, but they have been regarded ineffective. The reason\nis twofold. Firstly, the conventional way of capturing video using a single static\ncamera fails to capture focus and context. Secondly, a single static view is often\nmonotonous, making the video onerous to review. To address these issues, often\nhuman camera operators are employed to capture effective videos with changing\nviews, but this approach is expensive.\nIn this thesis, we argue that camera views can be changed automatically to\nproduce meeting videos effectively and inexpensively. We automate the camera view control by automatically determining the visual focus of attention as a function\nof time and moving the camera to capture it. In order to determine visual\nfocus of attention for different meetings, we conducted experiments and interviewed\ntelevision production professionals who capture meeting videos. Furthermore,\ntelevision production principles were used to appropriately frame shots\nand switch between shots.\nThe result of the evaluation of the automatic camera control system indicated\nits significant benefits over conventional static camera view. By applying television\nproduction principles various issues related to shot stability and screen\nmotion were resolved. The performance of the automatic camera control based\non television production principles also approached the performance of trained\nhuman camera crew. To further reduce the cost of the automation, we also explored\nthe application of computer vision and audio tracking.\nResults of our explorations provide empirical evidence in support of the utility\nof camera control encouraging future research in this area. Successful application\nof television production principles to automatically control cameras suggest\nvarious ways to handle issues involved in the automation process.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.090 | 0.023 |
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".