Searched for: subject%3A%22Drone%22
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Mink, Raoul (author)
Decentralised drone swarms need real time collision avoidance, thus requiring efficient, real time relative localisation. This paper explores different data inputs for vision based relative localisation. It introduces a novel dataset generated in <i>Blender</i>, providing ground truth optic flow and depth. Comparisons to <i>MPI Sintel</i>, an...
master thesis 2023
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Haifawi, Hani (author), Fioranelli, F. (author), Yarovoy, Alexander (author), van der Meer, Rob (author)
A new method to jointly detect and classify drones using a moving surveillance radar system (‘radar on-the-move’) and computer vision is presented. While most conventional counter-drone radar-based techniques focus on time-frequency distributions to obtain classification features, such approaches are limited in volumetric spatial coverage. To...
conference paper 2023
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Haifawi, Hani (author)
Drone detection and tracking systems are nowadays a requirement in most public, private and political events, because of the increasing risk of unintentional or malicious misuse of these platforms. Moreover, in order to ensure adequate protection, full spatial coverage is a must for every such system. However, the research literature focuses on...
master thesis 2022