• Login
    View Item 
    •   Home
    • IRAC Institute for Research in Applicable Computing - to April 2016
    • Centre for Research in Distributed Technologies (CREDIT)
    • View Item
    •   Home
    • IRAC Institute for Research in Applicable Computing - to April 2016
    • Centre for Research in Distributed Technologies (CREDIT)
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UOBREPCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalDepartmentThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalDepartment

    My Account

    LoginRegister

    About

    AboutLearning ResourcesResearch Graduate SchoolResearch InstitutesUniversity Website

    Statistics

    Display statistics

    Probabilistic target detection by camera-equipped UAVs

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Authors
    Symington, Andrew
    Waharte, Sonia
    Julier, Simon
    Trigoni, Niki
    Issue Date
    2010
    Subjects
    Bayesian methods
    robotics and automation
    robotics
    
    Metadata
    Show full item record
    Abstract
    This paper is motivated by the real world problem of search and rescue by unmanned aerial vehicles (UAVs). We consider the problem of tracking a static target from a bird's-eye view camera mounted to the underside of a quadrotor UAV. We begin by proposing a target detection algorithm, which we then execute on a collection of video frames acquired from four different experiments. We show how the efficacy of the target detection algorithm changes as a function of altitude. We summarise this efficacy into a table which we denote the observation model. We then run the target detection algorithm on a sequence of video frames and use parameters from the observation model to update a recursive Bayesian estimator. The estimator keeps track of the probability that a target is currently in view of the camera, which we refer to more simply as target presence. Between each target detection event the UAV changes position and so the sensing region changes. Under certain assumptions regarding the movement of the UAV, the proportion of new information may be approximated to a value, which we then use to weight the prior in each iteration of the estimator.
    Citation
    Symington, A.; Waharte, S.; Julier, S. and Trigoni, N., (2010) 'Probabilistic target detection by camera-equipped UAVs,' Robotics and Automation (ICRA), IEEE International Conference on: 4076-4081
    Publisher
    IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
    URI
    http://hdl.handle.net/10547/279160
    DOI
    10.1109/ROBOT.2010.5509355
    Additional Links
    http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5509355
    Type
    Conference papers, meetings and proceedings
    Language
    en
    ISBN
    9781424450381
    ae974a485f413a2113503eed53cd6c53
    10.1109/ROBOT.2010.5509355
    Scopus Count
    Collections
    Centre for Research in Distributed Technologies (CREDIT)

    entitlement

     
    DSpace software (copyright © 2002 - 2021)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.