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Extended image differencing for change detection in UAV video mosaics

: Saur, Günter; Krüger, Wolfgang; Schumann, A.

Postprint urn:nbn:de:0011-n-3013295 (5.7 MByte PDF)
MD5 Fingerprint: d7a80b2c47a53500cbdb1cb3785f49dd
Copyright 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: 21.4.2015

Loce, R.P. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.; Society for Imaging Science and Technology -IS&T-:
Video surveillance and transportation imaging applications 2014 : 3 - 5 February 2014, San Francisco, California, United States; proceedings
Bellingham, WA: SPIE, 2014 (Proceedings of SPIE 9026)
ISBN: 978-0-8194-9943-1
Paper 90260L, 10 S.
Conference "Video Surveillance and Transportation Imaging Applications" <2014, San Francisco/Calif.>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
change detection; image differencing; video mosaicking; UAV

Change detection is one of the most important tasks when using unmanned aerial vehicles (UAV) for video reconnaissance and surveillance. We address changes of short time scale, i.e. the observations are taken in time distances from several minutes up to a few hours. Each observation is a short video sequence acquired by the UAV in near-nadir view and the relevant changes are, e.g., recently parked or moved vehicles. In this paper we extend our previous approach of image differencing for single video frames to video mosaics. A precise image-to-image registration combined with a robust matching approach is needed to stitch the video frames to a mosaic. Additionally, this matching algorithm is applied to mosaic pairs in order to align them to a common geometry. The resulting registered video mosaic pairs are the input of the change detection procedure based on extended image differencing. A change mask is generated by an adaptive threshold applied to a linear combination of difference images of intensity and gradient magnitude. The change detection algorithm has to distinguish between relevant and non-relevant changes. Examples for non-relevant changes are stereo disparity at 3D structures of the scene, changed size of shadows, and compression or transmission artifacts. The special effects of video mosaicking such as geometric distortions and artifacts at moving objects have to be considered, too. In our experiments we analyze the influence of these effects on the change detection results by considering several scenes. The results show that for video mosaics this task is more difficult than for single video frames. Therefore, we extended the image registration by estimating an elastic transformation using a thin plate spline approach. The results for mosaics are comparable to that of single video frames and are useful for interactive image exploitation due to a larger scene coverage.