A Fast-Iterative Data Association Technique for Multiple Object Tracking
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Bibliographic record
Abstract
A massive amount of video data is recorded daily for forensic post analysis and computer vision applications. The analyses of this data often require multiple object tracking (MOT). Advancements in image analysis algorithms and global optimization techniques have improved the accuracy of MOT, often at the cost of slow processing speed which limits its applications only to small video datasets. With the focus on speed, a fast-iterative data association technique (FIDA) for MOT that uses a tracking-by-detection paradigm and finds a locally optimal solution with a low computational overhead is introduced. The performance analyses conducted on a set of benchmark video datasets show that the proposed technique is significantly faster (50–600 times) than the existing state-of-the-art techniques that produce a comparable tracking accuracy.
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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 | 0.004 | 0.002 |
| 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 | 0.001 |
| Open science | 0.002 | 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