[en] This paper presents a novel robust visual tracking framework, based on discrimi-
native method, for Unmanned Aerial Vehicles (UAVs) to track an arbitrary 2D/3D target at
real-time frame rates, that is called the Adaptive Multi-Classifier Multi-Resolution (AMCMR)
framework. In this framework, adaptive Multiple Classifiers (MC) are updated in the (k-
1)th frame-based Multiple Resolutions (MR) structure with compressed positive and negative
samples, and then applied them in the kth frame-based Multiple Resolutions (MR) structure to
detect the current target. The sample importance has been integrated into this framework to
improve the tracking stability and accuracy. The performance of this framework was evaluated
with the Ground Truth (GT) in different types of public image databases and real flight-
based aerial image datasets firstly, then the framework has been applied in the UAV to inspect
the Offshore Floating Platform (OFP). The evaluation and application results show that this
framework 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 illumination, partial/full target occlusion, blur motion, rapid pose variation
and onboard mechanical vibration, among others. To our best knowledge, this is the first work
to present this framework for solving the online learning and tracking freewill 2D/3D target
problems, and applied it in the UAVs.
Disciplines :
Aerospace & aeronautics engineering
Author, co-author :
Fu, Changhong; Universidad Politecnica de Madrid (UPM) - Centro de Automatica y Robotica (CAR) > Computer Vision Group
Suarez-Fernandez, Ramon; Universidad Politecnica de Madrid (UPM) - Centro de Automatica y Robotica (CAR) > Computer Vision Group
Olivares Mendez, Miguel Angel ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Campoy, Pascual; Universidad Politecnica de Madrid (UPM) - Centro de Automatica y Robotica (CAR) > Computer Vision Group
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