January 10, 2020 Conference paper Open Access

Baptista, Marcia; Fernandes, Luis; Chaves, Paulo

DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="DOI">10.5281/zenodo.3821145</identifier>
  <creators>
    <creator>
      <creatorName>Baptista, Marcia</creatorName>
      <givenName>Marcia</givenName>
      <familyName>Baptista</familyName>
      <affiliation>INOV Inesc Inovacao</affiliation>
    </creator>
    <creator>
      <creatorName>Fernandes, Luis</creatorName>
      <givenName>Luis</givenName>
      <familyName>Fernandes</familyName>
      <affiliation>INOV Inesc Inovacao</affiliation>
    </creator>
    <creator>
      <creatorName>Chaves, Paulo</creatorName>
      <givenName>Paulo</givenName>
      <familyName>Chaves</familyName>
      <affiliation>INOV Inesc Inovacao</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Tracking and Classification of Aerial Objects</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Object Tracking</subject>
    <subject>Deep Learning</subject>
    <subject>Residual Networks</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-01-10</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="ConferencePaper"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="doi">10.1007/978-3-030-38822-5_18</alternateIdentifier>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3821145</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3821144</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/alfa</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Unauthorized drone flying can prompt disruptions in critical facilities such as airports or railways. To prevent these situations, we propose a surveillance system that can sense malicious and/or illicit aerial targets. The idea is to track moving aerial objects using a static camera and when a tracked object is considered suspicious, the camera zooms in to take a snapshot of the target. This snapshot is then classified as an aircraft, drone, bird or cloud. In this work, we propose the classical technique of two-frame background subtraction to detect moving objects. We use the discrete Kalman filter to predict the location of each object and the Jonker-Volgenant algorithm to match objects between consecutive image frames. A deep residual network, trained with transfer learning, is used for image classification. The residual net ResNet-50 developed for the ILSVRC competition was retrained for this purpose. The performance of the system was evaluated with positive results in real-world conditions. The system was able to track multiple aerial objects with acceptable accuracy and the classification system also exhibited high performance.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/700002/">700002</awardNumber>
      <awardTitle>Advanced Low Flying Aircrafts Detection and Tracking</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>