A framework for detecting and tracking elephants in drone videos
Why this work is in the frame
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Bibliographic record
Abstract
The escalating global biodiversity crisis requires innovative and scalable solutions to monitor wildlife populations. Recent developments in remote sensing and deep learning offer promising avenues for improving the conservation of large mammals, including African elephants. This paper introduces a framework that utilizes drone video streams and integrates state-of-the-art object detection (YOLOv11) and tracking (BoT-SORT) methods, which are significantly enhanced by a custom post-track re-identification algorithm, to capture temporal dynamics and track individual elephants over time. The framework facilitates automated video analysis and elephant counting, generating key metrics such as individual elephant movement speed, group movement patterns, and Elephant Cluster Statistics. By automating aspects of data processing and analyses, this approach provides valuable insights that contribute to more efficient and data-driven decision-making in wildlife research.
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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