Robust real-time vision-based aircraft tracking from Unmanned Aerial Vehicles

Aircraft tracking plays a key and important role
in the Sense-and-Avoid system of Unmanned Aerial Vehicles
(UAVs). This paper presents a novel robust visual tracking
algorithm for UAVs in the midair to track an arbitrary aircraft
at real-time frame rates, together with a unique evaluation
system. This visual algorithm mainly consists of adaptive
discriminative visual tracking method, Multiple-Instance (MI)
learning approach, Multiple-Classifier (MC) voting mechanism
and Multiple-Resolution (MR) representation strategy, that is
called Adaptive M
3
tracker, i.e. AM
3
. In this tracker, the
importance of test sample has been integrated to improve
the tracking stability, accuracy and real-time performances.
The experimental results show that this algorithm is more
robust, efficient and accurate against the existing state-of-art
trackers, overcoming the problems generated by the challenging
situations such as obvious appearance change, variant sur-
rounding illumination, partial aircraft occlusion, blur motion,
rapid pose variation and onboard mechanical vibration, low
computation capacity and delayed information communication
between UAVs and Ground Station (GS). To our best knowledge,
this is the first work to present this tracker for solving online
learning and tracking freewill aircraft/intruder in the UAVs.