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Deep cross-domain flying object classification for robust UAV detection

 
: Schumann, A.; Sommer, L.; Klatte, J.; Schuchert, Tobias; Beyerer, Jürgen

:
Fulltext urn:nbn:de:0011-n-4819026 (909 KByte PDF)
MD5 Fingerprint: 0ea720cf3145f210ac586fd169219ce2
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Created on: 8.2.2018


Institute of Electrical and Electronics Engineers -IEEE-:
14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017 : August 29, 2017-September 1, 2017, Lecce
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5386-2939-0
ISBN: 978-1-5386-2940-6 (Print)
pp.587-592
International Conference on Advanced Video and Signal Based Surveillance (AVSS) <14, 2017, Lecce>
English
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
bird; camera; classification; neural network; object detection; security system; unmanned aerial vehicles (UAV)

Abstract
Recent progress in the development of unmanned aerial vehicles (UAVs) causes serious safety issues for mass events and safety-sensitive locations like prisons or airports. To address these concerns, robust UAV detection systems are required. In this work, we propose an UAV detection framework based on video images. Depending on whether the video images are recorded by static cameras or moving cameras, we initially detect regions that are likely to contain an object by median background subtraction or a deep learning based object proposal method, respectively. Then, the detected regions are classified into UAV or distractors, such as birds, by applying a convolutional neural network (CNN) classifier. To train this classifier, we use our own dataset comprised of crawled and self-acquired drone images, as well as bird images from a publicly available dataset. We show that, even across a significant domain gap, the resulting classifier can successfully identify UAVs in our target dataset. We evaluate our UAV detection framework on six challenging video sequences that contain UAVs at different distances as well as birds and background motion.

: http://publica.fraunhofer.de/documents/N-481902.html