WildLive: Near Real-time Visual Wildlife Tracking onboard UAVs

Live tracking of wildlife via high-resolution video process-
ing directly onboard drones is widely unexplored and most

existing solutions rely on streaming video to ground sta-
tions 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 in-
troduce 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

17.81 fps for HD and 7.53 fps on 4K video streams suit-
able 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 de-
tection and segmentation techniques. Essentially, compu-
tational resource is focused onto spatio-temporal regions

of high uncertainty to significantly improve UAV process-
ing speeds. Alongside, we introduce our WildLive dataset,

which comprises 200K+ annotated animal instances across

19K+ frames from 4K UAV videos collected at the Ol Pe-
jeta Conservancy in Kenya. All frames contain ground truth

bounding boxes, segmentation masks, as well as individ-
ual tracklets and tracking point trajectories. We compare

our system against current object tracking approaches in-
cluding 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. We publish all source code, weights,
dataset, and labels for easy utilisation by the community.