Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Spatio-temporal fusion of object segmentation approaches for moving distant targets

: Teutsch, Michael

Postprint urn:nbn:de:0011-n-2193983 (997 KByte PDF)
MD5 Fingerprint: 6624c7d10232a5d7db19c4f67c4f50b4
© 2012 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: 15.11.2012

Institute of Electrical and Electronics Engineers -IEEE-:
Fusion 2012, 15th International Conference on Information Fusion : 09.-15. July 2012, Singapore
New York, NY: IEEE, 2012
ISBN: 978-1-4673-0417-7 (Print)
ISBN: 978-0-9824438-4-2 (Online)
ISBN: 978-0-9824438-5-9
International Conference on Information Fusion (FUSION) <15, 2012, Singapore>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
moving target detection; independent motion; segmentation; surveillance; reconnaissance; UAV video

Object segmentation can be an important stepp between object detection and tracking. Especially in image processing applications, where object detection is difficult due to high object distance, camera motion, or noise, the detection result might not be precise enough to robustly initialize tracks and perform multi-target tracking. In this paper we present the detection and segmentation of moving objects in image sequences coming from a small Unmanned Aerial Vehicle (UAV). Based on the detection and tracking of local image features, camera motion is compensated and independent motion created by moving vehicles and people on the ground is found. By clustering the independent motion vectors initial object hypotheses are generated which may be affected by over- and under-segmentation. For improvement, several object segmentation approaches are introduced and tested. Best results are achieved with a spatiotemporal fusion of some approaches. Both spatial and temporal information is provided by the local image features. The object segmentation approaches and the fusion methods are evaluated for their completeness and precision.