无人机机载实时追踪野生动物，兼顾速度与精度。
WildLive: Near Real-time Visual Wildlife Tracking onboard UAVs
- 采用稀疏光流与任务特异性采样，聚焦高不确定性区域提升效率。
- 在4K视频上实现7.53fps处理速度，支持高空飞行减少干扰。
- 适用于无人值守导航和个体行为识别，适合生态保护应用。
通过高分辨率视频直接在无人机上进行野生动物实时追踪仍处于探索阶段，现有方案多依赖将视频流传输至地面站以支持导航。然而，超越视觉视距的自主动物响应飞行控制及任务特定的个体与行为识别，均需此能力。为此，我们提出WildLive——一种在无人机机载端运行的近实时动物检测与追踪框架，可处理高分辨率图像。系统在高清视频上实现17.81fps，在4K视频上达7.53fps，适用于高空飞行以减少对动物的干扰。该系统专为Jetson Orin AGX硬件优化，结合稀疏光流追踪、任务特异性采样与高效且经过验证的基于YOLO的目标检测与分割技术。计算资源集中于时空不确定性高的区域，显著提升处理速度。同时，我们发布了WildLive数据集，包含来自肯尼亚奥尔佩杰塔保护地的4K无人机视频中超过20万标注动物实例，覆盖19,000+帧，每帧含真实边界框、分割掩码、个体轨迹与追踪点路径。与OC-SORT、ByteTrack、SORT等主流追踪方法对比，实验验证了在机载硬件上实现近实时高分辨率野生动物追踪的可行性，并保持高精度，满足未来动物导向自主导航与任务执行需求。相关材料详见：https://dat-nguyenvn.github.io/WildLive/
原文摘要 · Abstract (English)
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 17.81fps for HD and 7.53fps 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. 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 multi-animal tracking experiments with onboard hardware confirm that near real-time high-resolution wildlife tracking is possible on UAVs whilst maintaining high accuracy levels as needed for future navigational and mission-specific animal-centric operational autonomy. Our materials are available at: https://dat-nguyenvn.github.io/WildLive/
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