Authors:
Eren Unlu
1
;
Emmanuel Zenou
1
and
Nicolas Riviere
2
Affiliations:
1
ISAE-SUPAERO, France
;
2
ONERA, France
Keyword(s):
Aerial Surveillance, Drone Detection, Generic Fourier Descriptor, Shape Descriptors, Object Recognition.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Pattern Recognition
;
Shape Representation
;
Software Engineering
Abstract:
With increasing number of Unmanned Aerial Vehicles (UAVs) -also known as drones- in our lives, safety and
privacy concerns have arose. Especially, strategic locations such as governmental buildings, nuclear power
stations etc. are under direct threat of these publicly available and easily accessible gadgets. Various methods
are proposed as counter-measure, such as acoustics based detection, RF signal interception, micro-doppler
RADAR etc. Computer vision based approach for detecting these threats seems as a viable solution due
to various advantages. We envision an autonomous drone detection and tracking system for the protection
of strategic locations. In this work, 2-dimensional scale, rotation and translation invariant Generic Fourier
Descriptor (GFD) features (which are analyzed with a neural network) are used for classifying aerial targets
as a drone or bird. For the training of this system, a large dataset composed of birds and drones is gathered
from open sources. We h
ave achieved up to 85.3% overall correct classification rate.
(More)