Real-time high-resolution omnidirectional imaging platform for drone detection and tracking

Autor: Yusuf Leblebici, Gokcen Nurlu, Jean-Philippe Thiran, Beat Ott, Bilal Demir, Peter Wellig, Vladan Popovic, Selman Ergunay
Rok vydání: 2019
Předmět:
Zdroj: Journal of Real-Time Image Processing. 17:1625-1635
ISSN: 1861-8219
1861-8200
DOI: 10.1007/s11554-019-00921-7
Popis: Drones have become steadily affordable, which raises privacy and security concerns as well as interest in drone detection systems. On the other hand, drone detection is a challenging task due to small dimensions of drones, difficulty of long-distance detection, strict real-time constraints and necessity of wide angle coverage for drones. Although different radar and audio-assisted drone detection systems have been presented, they suffer from the cost, range, or interference problems. On the contrary, a long-range detection can be obtained by a vision-based system. Aiming that, we propose a real-time moving object detection and tracking system optimized for drone detection using 16 cameras with 20 MP resolution. The proposed system detects drones from short range and long range with 360 $$^{\circ }$$ surveillance coverage owing high-performance ultra-high-resolution (320 MP) video-processing capability. It is able to detect drones with 100 cm diameter from 700 m distance despite deceptive background. It is interference free, so multiple systems can properly operate in the vicinity without effecting each other. It integrates processing power of embedded systems with flexibility of software to generate a full platform for drone detection and tracking.
Databáze: OpenAIRE
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