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FOLDOUT

Financiado

Through foliage detection including in the outermost regions of the EU

In the last years irregular migration has dramatically increased, and is no longer manageable with existing systems. Improved methods for border surveillance are necessary to ensure an effective and efficient EU border management.... ver más

31/08/2022

AIT

8M€

Presupuesto del proyecto: 8M€

Líder del proyecto

AIT AUSTRIAN INSTITUTE OF TECHNOLOGY GMBH No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 23 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-08-31

SEC-16-BES-2017: Through-foliage detection, includin...

I+D

Cerrada hace 7 años

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23 Participantes

AIT

1.70M€ | Lider

URALITA TEJADOS, SA

941.40K€ | Participante

THALES ALENIA SPACE FRANCE

225.31K€ | Participante

ONFI

168.74K€ | Participante

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Duración del proyecto: 52 meses Fecha Inicio: 2018-04-23
Fecha Fin: 2022-08-31

Líder del proyecto

AIT AUSTRIAN INSTITUTE OF TECHNOLOGY GMBH No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 8M€

Descripción del proyecto In the last years irregular migration has dramatically increased, and is no longer manageable with existing systems. Improved methods for border surveillance are necessary to ensure an effective and efficient EU border management. FOLDOUT focus is on through foliage detection in the inner and outermost regions of the EU. Foliage penetration is an unsolved important part of border surveillance. By solving the problem of unreliable detections in such harsh environments border guards’ workloads are reduced, costs are reduced and, last but not least, lives can be saved. Detecting people through dense foliage in extreme climates with only a penetration technology is prone to high fault rates. FOLDOUT will build a system that combines various sensors and technologies and intelligently fuses these into an effective and robust intelligent detection platform. Fusing several sensor signals increases the effectiveness of detection. Further, sensors will be influenced (i.e. detection parameters adapted) by events detected by other sensors in the vicinity. By integrating data, such as vehicle traffic, from outside the immediate border area pre-events can be detected and learned. The events will be analysed with machine learning tools to continuously increase the systems detection and tracking capability. FOLDOUT will make the tasks of Border Guards simpler and faster by combining events from various sensors to give a complete situation threat assessment combined with suggested reaction scenarios. A two year pilot in Bulgaria and demonstrators in Greece, Finland and French Guiana FOLDOUT will provide fundamental enhancements in the domain of border surveillance and improved search & rescue scenarios.

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