Live tracking of wildlife via high-resolution video processing directly onboard drones is widely unexplored and most existing solutions rely on streaming video to ground stations to support navigation. Yet, both autonomous animal-reactive flight control beyond visual line of sight and/or mission-specific individual and behaviour recognition tasks rely to some degree on this capability. In response, we introduce WildLive -- a near real-time animal detection and tracking framework for high-resolution imagery running directly onboard uncrewed aerial vehicles (UAVs). The system performs multi-animal detection and tracking at 17fps+ for HD and 7fps+ on 4K video streams suitable for operation during higher altitude flights to minimise animal disturbance. Our system is optimised for Jetson Orin AGX onboard hardware. It integrates the efficiency of sparse optical flow tracking and mission-specific sampling with device-optimised and proven YOLO-driven object detection and segmentation techniques. Essentially, computational resource is focused onto spatio-temporal regions of high uncertainty to significantly improve UAV processing speeds without domain-specific loss of accuracy. Alongside, we introduce our WildLive dataset, which comprises 200k+ annotated animal instances across 19k+ frames from 4K UAV videos collected at the Ol Pejeta Conservancy in Kenya. All frames contain ground truth bounding boxes, segmentation masks, as well as individual tracklets and tracking point trajectories. We compare our system against current object tracking approaches including OC-SORT, ByteTrack, and SORT. Our materials are available at: https://dat-nguyenvn.github.io/WildLive/

本研究解决了现有野生动物追踪技术在无人机上实时处理高分辨率视频的不足之处。提出的WildLive框架能够在无人机上实现每秒17帧以上的多动物检测与追踪，同时优化了计算资源，提高了处理速度。最重要的发现是，该系统能够在高空飞行中有效减少对动物的干扰，为自主反应性飞行控制和行为识别任务提供了新的可能性。

WildLive：无人机上的近实时视觉野生动物追踪