  We present an imitation learning method for autonomous drone patrolling based
only on raw videos. Different from previous methods, we propose to let the
drone learn patrolling in the air by observing and imitating how a human
navigator does it on the ground. The observation process enables the automatic
collection and annotation of data using inter-frame geometric consistency,
resulting in less manual effort and high accuracy. Then a newly designed neural
network is trained based on the annotated data to predict appropriate
directions and translations for the drone to patrol in a lane-keeping manner as
humans. Our method allows the drone to fly at a high altitude with a broad view
and low risk. It can also detect all accessible directions at crossroads and
further carry out the integration of available user instructions and autonomous
patrolling control commands. Extensive experiments are conducted to demonstrate
the accuracy of the proposed imitating learning process as well as the
reliability of the holistic system for autonomous drone navigation. The codes,
datasets as well as video demonstrations are available at
https://vsislab.github.io/uavpatrol
