A new approach to robust human motion detection
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents an image understanding approach to monitor human movement and identify the abnormal cir cumstance by robust motion detection for the care of the elderly in a home-based environment. In contrast to the conventional approaches which apply either a fixed feature extraction scheme or a fixed object model for motion de tection and tracking, we introduce a multiple feature ex traction scheme for robust motion detection. The proposed algorithms include 1) multiple image feature extraction in cluding the detection of interesting points and color clus ters, 2)adaptive thresholding selection based on the com pactness measures of fuzzy sets in image feature space, 3) a flexible model of human motion adapted in both rigid and non-rigid conditions, and 4) an optimized algorithm for ob ject tracking and fuzzy decision making.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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