January 10, 2020 Conference paper Open Access

Baptista, Marcia; Fernandes, Luis; Chaves, Paulo

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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>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.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "INOV Inesc Inovacao", 
      "@type": "Person", 
      "name": "Baptista, Marcia"
    }, 
    {
      "affiliation": "INOV Inesc Inovacao", 
      "@type": "Person", 
      "name": "Fernandes, Luis"
    }, 
    {
      "affiliation": "INOV Inesc Inovacao", 
      "@type": "Person", 
      "name": "Chaves, Paulo"
    }
  ], 
  "sameAs": [
    "https://doi.org/10.1007/978-3-030-38822-5_18"
  ], 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2020-01-10", 
  "headline": "Tracking and Classification of Aerial Objects", 
  "url": "https://zenodo.org/record/3821145", 
  "@type": "ScholarlyArticle", 
  "keywords": [
    "Object Tracking", 
    "Deep Learning", 
    "Residual Networks"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.3821145", 
  "@id": "https://doi.org/10.5281/zenodo.3821145", 
  "workFeatured": {
    "alternateName": "INTSYS 2019", 
    "location": "Braga, Portugal", 
    "@type": "Event", 
    "name": "3rd EAI International Conference on Intelligent Transport Systems"
  }, 
  "name": "Tracking and Classification of Aerial Objects"
}