THE EFFECTS OF CAPTURE CONDITIONS ON THE CAMSHIFT FACE TRACKER
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Face tracking - the continuous monitoring of head position, orientation, and geometry - has numerous practical applications for human-computer interaction, such as a perceptual form of multi-modal input. There are several non-invasive and computationally inexpensive techniques for face tracking that draw upon algorithms from computer vision. Of them, Bradski's CAMSHIFT algorithm is appealing because it requires minimal training. These techniques are particularly attractive in light of the growing installed base of fast desktop computers and cheap, low-end desktop digital video cameras. Low-end cameras, however, have characteristics that make them a poor fit for some such face tracking algorithms. In this paper, I introduce the problem of face tracking, provide an overview of the operation of CAMSHIFT as an example of a non-invasive vision-based face tracking algorithms, and describe my experiences attempting to employ video obtained from a low-end desktop digital video camera source in face tracking. I conclude this paper by offering conclusions and recommendations drawn upon my experiences.
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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 | 0.000 | 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 | 0.000 |
| 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 it