Conference paper Open Access

Tracking and Classification of Aerial Objects

Baptista, Marcia; Fernandes, Luis; Chaves, Paulo


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  <dc:creator>Baptista, Marcia</dc:creator>
  <dc:creator>Fernandes, Luis</dc:creator>
  <dc:creator>Chaves, Paulo</dc:creator>
  <dc:date>2020-01-10</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://zenodo.org/record/3821145</dc:identifier>
  <dc:identifier>10.5281/zenodo.3821145</dc:identifier>
  <dc:identifier>oai:zenodo.org:3821145</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/700002/</dc:relation>
  <dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-38822-5_18</dc:relation>
  <dc:relation>doi:10.5281/zenodo.3821144</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/alfa</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>Object Tracking</dc:subject>
  <dc:subject>Deep Learning</dc:subject>
  <dc:subject>Residual Networks</dc:subject>
  <dc:title>Tracking and Classification of Aerial Objects</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
</oai_dc:dc>
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