Conference paper Open Access

Tracking and Classification of Aerial Objects

Baptista, Marcia; Fernandes, Luis; Chaves, Paulo


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.3821145", 
  "language": "eng", 
  "title": "Tracking and Classification of Aerial Objects", 
  "issued": {
    "date-parts": [
      [
        2020, 
        1, 
        10
      ]
    ]
  }, 
  "abstract": "<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>", 
  "author": [
    {
      "family": "Baptista, Marcia"
    }, 
    {
      "family": "Fernandes, Luis"
    }, 
    {
      "family": "Chaves, Paulo"
    }
  ], 
  "id": "3821145", 
  "event-place": "Braga, Portugal", 
  "type": "paper-conference", 
  "event": "3rd EAI International Conference on Intelligent Transport Systems (INTSYS 2019)"
}
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