Bibliographic record
Abstract
This paper presents an algorithm for multiple-object tracking without using object detection. We concentrate on creating long-term trajectories for unknown moving objects by using a model-free tracking algorithm. Each individual object is tracked by modeling the temporal relationship between sequentially occurring local motion patterns. The algorithm is based on shape and motion descriptors of moving objects, obtained at two hierarchical levels from an event understanding system. By considering both local and global motion patterns, two sets of initial tracks, called linklets, are obtained. Then, a set of sparse tracks, referred to in the literature as tracklets, is produced by grouping linklets demonstrating similar motion patterns. This produces two sets of independent tracklets, referred to as the lowand high-level tracklets. We adopt Markov Chain Monte Carlo Data Association (MCMCDA) to estimate a varying number of trajectories given a set of tracklets as input. To this end, we formulate tracklet association as a Maximum A Posteriori (MAP) problem to create a chain of tracklets. The final output of the data association algorithm is a partition of the set of tracklets such that those belong to individual objects have been grouped. This yields individual tracks for each object in a video.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".