January 10, 2020 Conference paper Open Access

Baptista, Marcia; Fernandes, Luis; Chaves, Paulo

DCAT Export

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        <foaf:name>Baptista, Marcia</foaf:name>
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    <dct:title>Tracking and Classification of Aerial Objects</dct:title>
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    <dcat:keyword>Object Tracking</dcat:keyword>
    <dcat:keyword>Deep Learning</dcat:keyword>
    <dcat:keyword>Residual Networks</dcat:keyword>
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    <dct:description>&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;</dct:description>
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    <dct:title>Advanced Low Flying Aircrafts Detection and Tracking</dct:title>
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