Autonomous UAV Navigation for Individual Wildlife Re-Identification

2026-06-30

AI Summary

This study addresses the inefficiency and high cost of manual data collection in large-scale wildlife individual re-identification by proposing a task-aware embodied AI framework. For the first time, the framework integrates the downstream re-identification model’s image quality requirements for diagnostically informative body regions—such as the flank—into autonomous drone navigation decisions. The system combines YOLOv11 for object detection and a DINOv2-based pose classifier to guide the drone in real time to detect animals, adjust its heading, and approach until the target’s bounding box meets a predefined minimum size threshold. Validated on zebras in Kenya, the approach demonstrates effective performance and successfully generalizes to other species with distinctive coat patterns, including giraffes, tigers, and elephants.

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.

Problem

• wildlife re-identification
• autonomous UAV navigation
• image quality optimization
• individual identification
• ecological data collection

Innovation

• autonomous UAV navigation
• wildlife re-identification
• task-aware embodied AI
• pose-guided imaging
• DINOv2-based classifier