  Multi-camera full-body pose capture of humans and animals in outdoor
environments is a highly challenging problem. Our approach to it involves a
team of cooperating micro aerial vehicles (MAVs) with on-board cameras only.
The key enabling-aspect of our approach is the on-board person detection and
tracking method. Recent state-of-the-art methods based on deep neural networks
(DNN) are highly promising in this context. However, real time DNNs are
severely constrained in input data dimensions, in contrast to available camera
resolutions. Therefore, DNNs often fail at objects with small scale or far away
from the camera, which are typical characteristics of a scenario with aerial
robots. Thus, the core problem addressed in this paper is how to achieve
on-board, real-time, continuous and accurate vision-based detections using DNNs
for visual person tracking through MAVs. Our solution leverages cooperation
among multiple MAVs. First, each MAV fuses its own detections with those
obtained by other MAVs to perform cooperative visual tracking. This allows for
predicting future poses of the tracked person, which are used to selectively
process only the relevant regions of future images, even at high resolutions.
Consequently, using our DNN-based detector we are able to continuously track
even distant humans with high accuracy and speed. We demonstrate the efficiency
of our approach through real robot experiments involving two aerial robots
tracking a person, while maintaining an active perception-driven formation. Our
solution runs fully on-board our MAV's CPU and GPU, with no remote processing.
ROS-based source code is provided for the benefit of the community.
