Reference : Robust real-time vision-based aircraft tracking from Unmanned Aerial Vehicles
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Aerospace & aeronautics engineering
http://hdl.handle.net/10993/19155
Robust real-time vision-based aircraft tracking from Unmanned Aerial Vehicles
English
Fu, Changhong [Universidad Politecnica de Madrid]
Carrio, A. [Universidad Politecnica de Madrid]
Olivares Mendez, Miguel Angel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
Suarez-Fernandez, R. [Universidad Politecnica de Madrid]
Campoy, P. [Universidad Politecnica de Madrid]
May-2014
Robotics and Automation (ICRA), 2014 IEEE International Conference on
Yes
International
Robotics and Automation (ICRA), 2014 IEEE International Conference on
May 2014
[en] aircraft control ; autonomous aerial vehicles ; control engineering computing ; image motion analysis ; learning (artificial intelligence) ; mobile robots ; object tracking ; pose estimation ; robot vision ; stability ; Unmanned Aerial Vehicles ; adaptive M3 tracker ; adaptive discriminative visual tracking method
[en] 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.
http://hdl.handle.net/10993/19155
10.1109/ICRA.2014.6907659
5441-5446

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