Visual Multi-Face Tracking Applied to Council Proceedings
Why this work is in the frame
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Bibliographic record
Abstract
Face recognition, face measurement, camera control, measurement of expressions and other tasks can benefit from online visual multi-face tracking. Given the availability of high quality general purpose detectors and tracking-by-detection frameworks, we provide guidance on how to develop a multi-face tracker out of standard components. In this paper, we train common object detectors specifically on faces to understand how well these detectors perform and evaluate different classifier loss functions. Our specific case study tracks faces in the context of council meetings and in parliamentary settings such as the Canadian House of Commons for which we create an annotated video set as a benchmark (see Fig. 1). These meetings in a parliamentary setting are often recorded from multiple cameras with participants and audiences walking around. Fast camera switching and zooming lead to significant scale changes of faces. Therefore, these settings can be characterized as tracking in unconstrained video. This will negatively impact the tracking accuracy and increase the likelihood of identity switches (IDS) between face labels. However, being able to track in unconstrained video enables a wider range of measurement applications. We find that while online tracking based on combining state-of-the-art methods can lead to high-quality tracking results, there is still a large gap between offline and online methods. The discussed method can be adapted to other tracking tasks for which large image databases are available.
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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 | 0.001 |
| 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 | 0.001 |
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 it