Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Flying object detection for automatic UAV recognition

: Sommer, L.; Schumann, A.; Müller, Thomas; Schuchert, Tobias; Beyerer, Jürgen

Volltext urn:nbn:de:0011-n-4819052 (1.6 MByte PDF)
MD5 Fingerprint: a420b5f45517a6bc0ccef6a0ea4b3d3e
© IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Erstellt am: 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)
International Conference on Advanced Video and Signal Based Surveillance (AVSS) <14, 2017, Lecce>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
object proposal detectors; flying object detection; automatic UAV recognition; automatic UAV detection system; unmanned aerial vehicle; security service; video imagery; video data; static cameras; moving camera; image differencing; convolutional neural network; CNN

With the increasing use of unmanned aerial vehicles (UAVs) by consumers, automatic UAV detection systems have become increasingly important for security services. In such a system, video imagery is a core modality for the detection task, because it can cover large areas and is very cost-effective to acquire. Many detection systems consist of two parts: flying object detection and subsequent object classification. In this work, we investigate the suitability of a number of flying object detection approaches for the task of UAV detection based on video data from static and moving cameras. We compare approaches based on image differencing with object proposal detectors which are learned from data. Finally, we classify each detection by a convolutional neural network (CNN) into the classes UAV or clutter. Our approach is evaluated on six sequences of challenging real world data which contain multiple UAVs, birds, and background motion.