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Detection and classification of moving objects from UAVs with optical sensors

: Teutsch, M.; Krüger, W.; Heinze, N.

Postprint urn:nbn:de:0011-n-1678852 (6.7 MByte PDF)
MD5 Fingerprint: 8537aeda0b0269f24731a5f99f3a9bfa
Copyright 2011 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Erstellt am: 15.10.2011

Kadar, I. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Signal processing, sensor fusion, and target recognition XX : 25-27 April 2011, Orlando, Florida
Bellingham, WA: SPIE, 2011 (Proceedings of SPIE 8050)
ISBN: 978-0-8194-8624-0
Paper 80501J, 14 S.
Conference "Signal Processing, Sensor Fusion, and Target Recognition" <20, 2011, Orlando/Fla.>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
video exploitation; UAV; moving object detection; tracking; classification; surveillance

Small and medium sized UAVs like German LUNA have long endurance and define in combination with sophisticated image exploitation algorithms a very cost efficient platform for surveillance. At Fraunhofer IOSB, we have developed the video exploitation system ABUL with the target to meet the demands of small and medium sized UAVs. Several image exploitation algorithms such as multi-resolution, super-resolution, image stabilization, geocoded mosaiking and stereo-images/3D-models have been implemented and are used with several UAV-systems. Among these algorithms is the moving target detection with compensation of sensor motion. Moving objects are of major interest during surveillance missions, but due to movement of the sensor on the UAV and small object size in the images, it is a challenging task to develop reliable detection algorithms under the constraint of real-time demands on limited hardware resources. Based on compensation of sensor motion by fast and robust estimation of geometric transformations between images, independent motion is detected relatively to the static background. From independent motion cues, regions of interest (bounding-boxes) are generated and used as initial object hypotheses. A novel classification module is introduced to perform an appearance-based analysis of the hypotheses. Various texture features are extracted and evaluated automatically for achieving a good feature selection to successfully classify vehicles and people.