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Change detection in crowded underwater scenes - via an extended Gaussian switch model combined with a flux tensor Pre-segmentation

: Radolko, Martin; Farhadifard, Fahimeh; Lukas, Uwe von

Postprint urn:nbn:de:0011-n-4415092 (30 MByte PDF)
MD5 Fingerprint: 1e573168f5f3e045c00a25e1f6859b1d
Created on: 20.9.2017

Imai, Francisco (Ed.) ; Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal:
12th International Conference on Computer Vision Theory and Applications, VISIGRAPP 2017. Proceedings. Vol.4: VISAPP : February 27-1, 2017, in Porto, Portugal
SciTePress, 2017
ISBN: 978-989-758-225-7
International Joint Conference on Computer Vision and Computer Graphics Theory and Applications (VISIGRAPP) <12, 2017, Porto>
International Conference on Computer Vision Theory and Applications (VISAPP) <12, 2017, Porto>
Conference Paper, Electronic Publication
Fraunhofer IGD ()
optical flow; motion segmentation; video segmentation; underwater imaging; Guiding Theme: Digitized Work; Research Area: Computer vision (CV)

In this paper a new approach for change detection in videos of crowded scenes is proposed with the extended Gaussian Switch Model in combination with a Flux Tensor pre-segmentation. The extended Gaussian Switch Model enhances the previous method by combining it with the idea of the Mixture of Gaussian approach and an intelligent update scheme which made it possible to create more accurate background models even for difficult scenes. Furthermore, a foreground model was integrated and could deliver valuable information in the segmentation process. To deal with very crowded areas in the scene - where the background is not visible most of the time - we use the Flux Tensor to create a first coarse segmentation of the current frame and only update areas that are almost motionless and therefore with high certainty should be classified as background. To ensure the spatial coherence of the final segmentations, the N2Cut approach is added as a spatial model after the background subtraction step. The evaluation was done on an underwater change detection datasets and showed significant improvements over previous methods, especially in the crowded scenes.