Real-Time Adaptive Multi-Classifier Multi-Resolution Visual Tracking Framework for Unmanned Aerial Vehicles

Fu, Changhong and Suárez Fernández, Ramón and Olivares Méndez, Miguel Ángel and Campoy Cervera, Pascual (2013). Real-Time Adaptive Multi-Classifier Multi-Resolution Visual Tracking Framework for Unmanned Aerial Vehicles. In: "2nd IFAC Workshop on Research, Education and Development of Unmanned Aerial Systems", 20-22 Nov 2013, Compiegne, France. ISBN 978-3-902823-57-1. pp. 99-106.

Description

Title: Real-Time Adaptive Multi-Classifier Multi-Resolution Visual Tracking Framework for Unmanned Aerial Vehicles
Author/s:
  • Fu, Changhong
  • Suárez Fernández, Ramón
  • Olivares Méndez, Miguel Ángel
  • Campoy Cervera, Pascual
Item Type: Presentation at Congress or Conference (Article)
Event Title: 2nd IFAC Workshop on Research, Education and Development of Unmanned Aerial Systems
Event Dates: 20-22 Nov 2013
Event Location: Compiegne, France
Title of Book: 2nd IFAC Workshop on Research, Education and Development of Unmanned Aerial Systems
Date: 2013
ISBN: 978-3-902823-57-1
Subjects:
Freetext Keywords: Unmanned Aerial Vehicles(UAVs), Discriminative Visual Tracking(DVT), Hierarchical Tracking Strategy(HTS), Online Appearance Learning(OAL), Compressive Visual Sensing(CVS), Adaptive Algorithm, Robot Navigation.
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Electrónica e Informática Industrial [hasta 2014]
UPM's Research Group: Computer Vision Group
Creative Commons Licenses: None

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview

Abstract

This paper presents a novel robust visual tracking framework, based on discriminative 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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainPI2010-20751-C02-01CICYTUnspecifiedUnspecified
FP7FP7-ICT-231143ECHORDUnspecifiedOMNIWORKS project

More information

Item ID: 37650
DC Identifier: http://oa.upm.es/37650/
OAI Identifier: oai:oa.upm.es:37650
Official URL: http://www.ifac-papersonline.net/
Deposited by: Memoria Investigacion
Deposited on: 14 Sep 2015 15:40
Last Modified: 16 Sep 2015 15:42
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM