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Elchik, CC, Wich, S 
ORCID: 0000-0003-3954-5174 and Burger, A
(2025)
A framework for detecting and tracking elephants in drone videos.
Drone Systems and Applications, 13.
pp. 1-16.
ISSN 2564-4939
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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-theart 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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | object detection; object tracking; re-identification; drone videos; wildlife conservation; YOLO; 46 Information and Computing Sciences; Networking and Information Technology R&D (NITRD); Machine Learning and Artificial Intelligence; Data Science; Generic health relevance; 15 Life on Land |
| Subjects: | Q Science > QL Zoology T Technology > T Technology (General) |
| Divisions: | Biological and Environmental Sciences (from Sep 19) |
| Publisher: | Canadian Science Publishing |
| Date of acceptance: | 30 September 2025 |
| Date of first compliant Open Access: | 10 June 2026 |
| Date Deposited: | 10 Jun 2026 10:21 |
| Last Modified: | 10 Jun 2026 10:21 |
| DOI or ID number: | 10.1139/dsa-2025-0032 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/28795 |
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