Head pose estimation and its application in TV viewers' behavior analysis
Why this work is in the frame
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Bibliographic record
Abstract
Head pose implies a person's visual attention and interest. It plays an important role in many applications. Existing head pose estimation methods work in the original head pose space. However, the large number of head pose candidates in the space makes the estimation task quite challenging. In this paper, we propose a coarse-to-fine head pose estimation method by decomposing the original pose space into a hierarchical structure. The estimation begins by detecting the region of interest (ROI) within a face image via measuring the importance scores of key image points. After that, a coarse head pose estimation step is applied to identify a subset of head pose candidates, based on Gabor filter and random forest. A fine estimation is then employed within the subset, using histogram of oriented gradient (HOG) and support vector machine (SVM). Finally, we apply the proposed method to TV viewers' behavior analysis by determining whether a viewer is focused or unfocused, which can be useful for marketing research.
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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 | 0.001 |
| 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