Autonomous UAV Navigation for Individual Wildlife Re-Identification

• URL: http://arxiv.org/abs/2606.31772v1
• Date: Tue, 30 Jun 2026 14:56:04 GMT
• Title: Autonomous UAV Navigation for Individual Wildlife Re-Identification
• Abstract summary: We propose an autonomous drone navigation system that actively optimize image capture for downstream re-ID.<n>The system combines YOLOv11 object detection with a DINOv2-based pose to guide real-time flight decisions.<n>We show the approach generalizes to other species with diagnostic surface patterns, including giraffes, tigers, and elephants.
• Score: 0.350327094468706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reliable individual re-identification (re-ID) of wildlife is essential for population monitoring, behavioral tracking, and conservation policy evaluation, yet large-scale data collection remains labor-intensive, relying on manual efforts by ecologists or citizen scientists. We propose an autonomous drone navigation system that actively optimizes image capture for downstream re-ID, moving beyond passive aerial sensing. The system combines YOLOv11 object detection with a DINOv2-based pose classifier to guide real-time flight decisions: detecting animals, orienting to expose the lateral flank (the surface of interest for pattern-based re-ID), and approaching until the subject meets a minimum bounding-box threshold. Unlike prior drone systems that optimize for group-level behavioral video, ours targets the specific image-quality requirements of individual-identification models. We demonstrate feasibility through a case study on zebra using footage collected in Kenya, and show the approach generalizes to other species with diagnostic surface patterns, including giraffes, tigers, and elephants. Our work establishes a framework for task-aware embodied AI for ecological data collection, in which downstream re-ID requirements drive real-time perception and control.

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