Tracking algorithm based on spatial progressive matching strategy and optimized correlation
Why this work is in the frame
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Bibliographic record
Abstract
The task of multi-target tracking is to correctly associate the identity of the same target in the two scene scenes. How to improve the accuracy of target tracking is still full of challenges. In this article, we propose a tracking algorithm based on spatial progressive matching strategy and optimized correlation. In it, we divide the targets in the scene according to the area of the target detection frame in the scene. By Prioritizing matching of target groups with a larger target frame area, and then matching target groups with a smaller target frame area is the spatial progressive matching strategy we propose. We noticed that in certain scenes where the target moves too quickly, the traditional intersection-to-union method becomes limited to some extent. Therefore, we substituted it with a circular intersection-to-union ratio method, which is more effective in accurately associating the targets in those scenes.
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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