  Drone equipped with cameras can dynamically track the target in the air from
a broader view compared with static cameras or moving sensors over the ground.
However, it is still challenging to accurately track the target using a single
drone due to several factors such as appearance variations and severe
occlusions. In this paper, we collect a new Multi-Drone single Object Tracking
(MDOT) dataset that consists of 92 groups of video clips with 113,918 high
resolution frames taken by two drones and 63 groups of video clips with 145,875
high resolution frames taken by three drones. Besides, two evaluation metrics
are specially designed for multi-drone single object tracking, i.e. automatic
fusion score (AFS) and ideal fusion score (IFS). Moreover, an agent sharing
network (ASNet) is proposed by self-supervised template sharing and view-aware
fusion of the target from multiple drones, which can improve the tracking
accuracy significantly compared with single drone tracking. Extensive
experiments on MDOT show that our ASNet significantly outperforms recent
state-of-the-art trackers.
