Towards video based collective motion analysis through shape tracking and matching
Bibliographic record
Abstract
The term ‘collective’ is used to refer to a group of animals such as a flock of birds or a herd of elephants. Aggregate motions of such collectives often give rise to visually pleasing shapes and patterns (e.g. V‐shape formation of geese while they migrate from one place to another). While shapes of moving collectives are of great interest in many scientific studies, scant attention has been given to algorithmically extract and render these shapes via polygonal boundaries or graphs. In this Letter, the authors present a multi‐stage, proof of concept framework for tracking geometric shapes and extracting video frames containing a user defined shape of moving collectives, by employing a deep‐learning based object detection, well‐known alpha shapes and a modified shape context. They demonstrate the usefulness of the proposed framework on a couple of test videos and discuss its potential applications in a wider area.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".